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data-trace="false" data-dom-id="Pill-react-component-cbf849b8-0b76-42e4-b363-d2387aa9c7e0"></div> <div id="Pill-react-component-cbf849b8-0b76-42e4-b363-d2387aa9c7e0"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Journal-papers" data-toggle="tab" href="#journalpapers" role="tab" title="Journal papers"><span>1</span> <span class="ds2-5-body-sm-bold">Journal papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>654</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Journal papers" id="Journal papers"><h3 class="profile--tab_heading_container">Journal papers by Peter Willett</h3></div><div class="js-work-strip profile--work_container" data-work-id="3809678"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/3809678/One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion"><img alt="Research paper thumbnail of One-bit Decentralized Detection with a Rao Test for Multisensor Fusion" class="work-thumbnail" src="https://attachments.academia-assets.com/31465115/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/3809678/One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion">One-bit Decentralized Detection with a Rao Test for Multisensor Fusion</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/WillettJohn">Peter Willett</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://unina.academia.edu/DomenicoCiuonzo">Domenico Ciuonzo</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this letter we propose the Rao test as a simpler alternative to the generalized likelihood rat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4eb3b0c10b92e526e2eb4dcb0e3941fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31465115,"asset_id":3809678,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31465115/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="3809678"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="3809678"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 3809678; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=3809678]").text(description); $(".js-view-count[data-work-id=3809678]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 3809678; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='3809678']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 3809678, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4eb3b0c10b92e526e2eb4dcb0e3941fa" } } $('.js-work-strip[data-work-id=3809678]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":3809678,"title":"One-bit Decentralized Detection with a Rao Test for Multisensor Fusion","translated_title":"","metadata":{"abstract":"In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Peter Willett</h3></div><div class="js-work-strip profile--work_container" data-work-id="124838196"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838196/An_extended_target_tracking_model_with_multiple_random_matrices_and_unified_kinematics"><img alt="Research paper thumbnail of An extended target tracking model with multiple random matrices and unified kinematics" class="work-thumbnail" src="https://attachments.academia-assets.com/118990277/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838196/An_extended_target_tracking_model_with_multiple_random_matrices_and_unified_kinematics">An extended target tracking model with multiple random matrices and unified kinematics</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 6, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="98c89bb8f83f0e952cc4e75060a26ace" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990277,"asset_id":124838196,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990277/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838196"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838196"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838196; 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A gamma Gaussian inverse Wishart implementation is derived, and necessary approximations are suggested to alleviate the data association complexity. 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class="js-work-strip profile--work_container" data-work-id="124838195"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods"><img alt="Research paper thumbnail of To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods" class="work-thumbnail" src="https://attachments.academia-assets.com/118990276/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods">To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 11, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f60303c55da7f48de7880892bdf692ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990276,"asset_id":124838195,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838195"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838195"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838195; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838195]").text(description); $(".js-view-count[data-work-id=124838195]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838195; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838195']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838195, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f60303c55da7f48de7880892bdf692ff" } } $('.js-work-strip[data-work-id=124838195]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838195,"title":"To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods","translated_title":"","metadata":{"publisher":"Cornell University","grobid_abstract":"Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.","publication_date":{"day":11,"month":8,"year":2023,"errors":{}},"publication_name":"arXiv (Cornell University)","grobid_abstract_attachment_id":118990276},"translated_abstract":null,"internal_url":"https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods","translated_internal_url":"","created_at":"2024-10-18T09:25:53.895-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118990276,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990276/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990276/2308-libre.pdf?1729270582=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=Tpizq0Fnil9Y5D9vD~aqjyZVa~GUUE6kn-aKGv2im4VWr3LcQ-tS9JWdAaKW8kErJpagfkP3YVqfWrTeKea8jwwf6GKmC-0oPyV3ZgW-vBV1Y~eoGc2Ns8fE2PbeYIgoDIjdtXLsqozz4IDKNTYaSfWD0rx88x~Z3Gr6xmIq3n48JvVtL0z~WHF9GM~vihi-lxFAb-NEdhP066eWgyVsEix9ccqtjud5n3OhcCPd9zAMyJDV2kE1NqxXTSjkgxfLreZ8KNsu9sRK6qxhBoHRoe~vRXEnxiBD-8nlyDGja5eQQZwQb2LbA~cB0bfPuqRb~GBB-OxSzoaXyhR~4sRKnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":118990276,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990276/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990276/2308-libre.pdf?1729270582=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=Tpizq0Fnil9Y5D9vD~aqjyZVa~GUUE6kn-aKGv2im4VWr3LcQ-tS9JWdAaKW8kErJpagfkP3YVqfWrTeKea8jwwf6GKmC-0oPyV3ZgW-vBV1Y~eoGc2Ns8fE2PbeYIgoDIjdtXLsqozz4IDKNTYaSfWD0rx88x~Z3Gr6xmIq3n48JvVtL0z~WHF9GM~vihi-lxFAb-NEdhP066eWgyVsEix9ccqtjud5n3OhcCPd9zAMyJDV2kE1NqxXTSjkgxfLreZ8KNsu9sRK6qxhBoHRoe~vRXEnxiBD-8nlyDGja5eQQZwQb2LbA~cB0bfPuqRb~GBB-OxSzoaXyhR~4sRKnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":118990274,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990274/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990274/download_file","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990274/2308-libre.pdf?1729270588=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=N9q5wytYNugkUjxoawLuGHMs7EE4ag4Iy1eUthDvNfLKOS~udhsw4bdolB4aAYNInXFkbTCPzR7eppAQQcSR~Fbx7PYZS5nuX-aURjkL3JClHycS-fZPm~YN4NITpuKwH3gF4eC-lLpsknL4Wtz06fqkUFNpU8jwbTl-YAjnF6DtoFsviE6MwixUKdIfsCPRBpz6nCOFI89Ps0cdSQXu3IBuQL8KmmQqdMTkVRVcdxEcC2p9q79fr68pTM~QfRP4yrUr6tJH0blhapxxygsT5zBQrAOxlch1DLMUwNIgq~mtg1X~3m7doEEH2kXiGAtxddT7Kol8OXWENpwwtOTrDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":278182,"name":"Data Association","url":"https://www.academia.edu/Documents/in/Data_Association"},{"id":320537,"name":"Belief Propagation","url":"https://www.academia.edu/Documents/in/Belief_Propagation"}],"urls":[{"id":45214443,"url":"https://arxiv.org/pdf/2308.06326"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838194"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838194/3D_Localization_and_Tracking_Methods_for_Multi_Platform_Radar_Networks"><img alt="Research paper thumbnail of 3D Localization and Tracking Methods for Multi-Platform Radar Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/118990275/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838194/3D_Localization_and_Tracking_Methods_for_Multi_Platform_Radar_Networks">3D Localization and Tracking Methods for Multi-Platform Radar Networks</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 14, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdfb869ff9f883c41d767858c45a8830" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990275,"asset_id":124838194,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990275/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838194"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838194"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838194; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838194]").text(description); $(".js-view-count[data-work-id=124838194]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838194; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838194']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838194, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cdfb869ff9f883c41d767858c45a8830" } } $('.js-work-strip[data-work-id=124838194]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838194,"title":"3D Localization and Tracking Methods for Multi-Platform Radar Networks","translated_title":"","metadata":{"publisher":"Cornell University","ai_title_tag":"3D Localization and Tracking in Multi-Platform Radar Networks","grobid_abstract":"Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario.","publication_date":{"day":14,"month":8,"year":2023,"errors":{}},"publication_name":"arXiv (Cornell 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src="https://attachments.academia-assets.com/118990268/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838190/Scalable_multitarget_tracking_using_multiple_sensors_A_belief_propagation_approach">Scalable multitarget tracking using multiple sensors: A belief propagation approach</a></div><div class="wp-workCard_item"><span>2015 18th International Conference on Information Fusion (Fusion)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a method for multisensor-multitarget tracking with excellent scalability in the number...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a “detailed” factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. In particular, we observed that our method can outperfo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="097e6499b7db36a5e42ea03be7a69adc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990268,"asset_id":124838190,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990268/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838190"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838190]").text(description); $(".js-view-count[data-work-id=124838190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838190']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838190, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "097e6499b7db36a5e42ea03be7a69adc" } } $('.js-work-strip[data-work-id=124838190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838190,"title":"Scalable multitarget tracking using multiple sensors: A belief propagation approach","translated_title":"","metadata":{"abstract":"We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a “detailed” factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838189/Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method"><img alt="Research paper thumbnail of Tracking an unknown number of targets using multiple sensors: A belief propagation method" class="work-thumbnail" src="https://attachments.academia-assets.com/118990266/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838189/Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method">Tracking an unknown number of targets using multiple sensors: A belief propagation method</a></div><div class="wp-workCard_item"><span>2016 19th International Conference on Information Fusion (FUSION)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a multisensor method for tracking an unknown number of targets. Low computational comp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a multisensor method for tracking an unknown number of targets. Low computational complexity and very good scalability in the number of targets, number of sensors, and number of measurements per sensor are achieved by running a belief propagation (BP) message passing scheme on a suitably devised factor graph. Using a redundant formulation of data association uncertainty and “augmented target states” including target indicators allows the proposed BP method to leverage statistical independencies for a drastic reduction of complexity. The proposed method is shown to outperform previously proposed multisensor methods for multitarget tracking, including methods with a less favorable scaling behavior.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="936214ab20e689de8d3cec4b14324095" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990266,"asset_id":124838189,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990266/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838189]").text(description); $(".js-view-count[data-work-id=124838189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838189, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "936214ab20e689de8d3cec4b14324095" } } $('.js-work-strip[data-work-id=124838189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838189,"title":"Tracking an unknown number of targets using multiple sensors: A belief propagation method","translated_title":"","metadata":{"abstract":"We propose a multisensor method for tracking an unknown number of targets. 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The proposed method is shown to outperform previously proposed multisensor methods for multitarget tracking, including methods with a less favorable scaling behavior.","internal_url":"https://www.academia.edu/124838189/Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method","translated_internal_url":"","created_at":"2024-10-18T09:25:49.327-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118990266,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990266/thumbnails/1.jpg","file_name":"publik_255582.pdf","download_url":"https://www.academia.edu/attachments/118990266/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Tracking_an_unknown_number_of_targets_us.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990266/publik_255582-libre.pdf?1729270579=\u0026response-content-disposition=attachment%3B+filename%3DTracking_an_unknown_number_of_targets_us.pdf\u0026Expires=1732780710\u0026Signature=cRJsyx~GE~pgoAe3kC~IZZV7vluREYGi5vx5XKOT5WD9kB9EwotYTCNx7H8nOdSoebvGwWJl-Wxn5zsEuBvq~dtrsp1deXkcPSnPgXZE04Bq56I-nEns-SZYcwFTnhCBPm~FH7wrbk2EdnUswAuRqCIJIwmjxwkq0s0UVy4HAbaaigl~9m6CMLmYFXtBAGVe-bk2NKaUXzQhaLBN250ZPYDwfLJXvvdrvp7Z0rCMIcwRsa56p6OC~dAcXksJtE-RCs-K4-M8nZDM1Q55KFpIcMI0EJMA-wMiAi9wE8v5TL1~rWPVdO69mhBGNzDNXuIXVekknPqqFonb-WrtQn-0WQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":118990266,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990266/thumbnails/1.jpg","file_name":"publik_255582.pdf","download_url":"https://www.academia.edu/attachments/118990266/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Tracking_an_unknown_number_of_targets_us.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990266/publik_255582-libre.pdf?1729270579=\u0026response-content-disposition=attachment%3B+filename%3DTracking_an_unknown_number_of_targets_us.pdf\u0026Expires=1732780710\u0026Signature=cRJsyx~GE~pgoAe3kC~IZZV7vluREYGi5vx5XKOT5WD9kB9EwotYTCNx7H8nOdSoebvGwWJl-Wxn5zsEuBvq~dtrsp1deXkcPSnPgXZE04Bq56I-nEns-SZYcwFTnhCBPm~FH7wrbk2EdnUswAuRqCIJIwmjxwkq0s0UVy4HAbaaigl~9m6CMLmYFXtBAGVe-bk2NKaUXzQhaLBN250ZPYDwfLJXvvdrvp7Z0rCMIcwRsa56p6OC~dAcXksJtE-RCs-K4-M8nZDM1Q55KFpIcMI0EJMA-wMiAi9wE8v5TL1~rWPVdO69mhBGNzDNXuIXVekknPqqFonb-WrtQn-0WQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":118990267,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990267/thumbnails/1.jpg","file_name":"publik_255582.pdf","download_url":"https://www.academia.edu/attachments/118990267/download_file","bulk_download_file_name":"Tracking_an_unknown_number_of_targets_us.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990267/publik_255582-libre.pdf?1729270580=\u0026response-content-disposition=attachment%3B+filename%3DTracking_an_unknown_number_of_targets_us.pdf\u0026Expires=1732780710\u0026Signature=VauaEmb5vEJWzPQGd~QtWJTTgXRcqO8IjXPWa8-4F8sIj9olS8jE5mTqhd-o9p9BcTXDrB~01q4N7r2vr1H9FjaOAHZsWmDbTxC-A0JC3Aqf8r1SmjWki-2SvlLo8Fj0Mew~k1ocAT4Ph~ELKDxRl62qCCVVo7-OnGE9jVrf2sGkEa-jWP1NBrtWt1qP68d3bhMuiA85oph4E-pAys1jAUfS1bh6HsCOw6MCamViM-vZP2-1enSqZUn4qwB7awIUjxrkYGqVKuQIO408f9gJmZBovy-Z~NXgzJPDL9a6vUCBhqq0-1kZpHeSIdC7uZYgmr0NSr5mQSirjSl7Gn690A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing"},{"id":278182,"name":"Data Association","url":"https://www.academia.edu/Documents/in/Data_Association"},{"id":320537,"name":"Belief Propagation","url":"https://www.academia.edu/Documents/in/Belief_Propagation"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability"},{"id":2537936,"name":"Factor Graph","url":"https://www.academia.edu/Documents/in/Factor_Graph"}],"urls":[{"id":45214438,"url":"https://publik.tuwien.ac.at/files/publik_255582.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838188/Signal_Amplitude_Estimation_and_Detection_From_Unlabeled_Binary_Quantized_Samples"><img alt="Research paper thumbnail of Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples" class="work-thumbnail" src="https://attachments.academia-assets.com/118990294/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838188/Signal_Amplitude_Estimation_and_Detection_From_Unlabeled_Binary_Quantized_Samples">Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Signal Processing</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="051c387bc2c26d01f096511410d02a59" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990294,"asset_id":124838188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990294/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838188; 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First, maximum likelihood (ML) estimators are utilized to estimate both the permutation matrix and unknown signal amplitude under arbitrary, but known signal shape and quantizer thresholds. Sufficient conditions are provided under which an ML estimator can be found in polynomial time and an alternating maximization algorithm is proposed to solve the general problem via good initial estimates. In addition, the statistical identifiability of the model is studied. Furthermore, the generalized likelihood ratio test (GLRT) detector is adopted to detect the presence of signal. In addition, an accurate approximation to the probability of successful permutation matrix recovery is derived, and explicit expressions are provided to reveal the relationship between the number of signal samples and the number of quantizers. 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Our method achieves low computational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of \"augmented target states\" including binary target indicators make it possible to exploit statistical independencies for a drastic reduction of complexity. An increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. As a consequence, the complexity of our method scales only quadratically in the number of targets, linearly in the number of sensors, and linearly in the number of measurements per sensors. The performance of the method compares well with that of previously proposed methods, including methods with a less favorable scaling behavior. In particular, our method can outperform multisensor versions of the probability hypothesis density (PHD) filter, the cardinalized PHD filter, and the multi-Bernoulli filter.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"IEEE Transactions on Signal Processing","grobid_abstract_attachment_id":118990281},"translated_abstract":null,"internal_url":"https://www.academia.edu/124838171/A_Scalable_Algorithm_for_Tracking_an_Unknown_Number_of_Targets_Using_Multiple_Sensors","translated_internal_url":"","created_at":"2024-10-18T09:25:00.839-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118990281,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990281/thumbnails/1.jpg","file_name":"1607.pdf","download_url":"https://www.academia.edu/attachments/118990281/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Scalable_Algorithm_for_Tracking_an_Unk.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990281/1607-libre.pdf?1729270581=\u0026response-content-disposition=attachment%3B+filename%3DA_Scalable_Algorithm_for_Tracking_an_Unk.pdf\u0026Expires=1732780710\u0026Signature=ShxEJr05tOIrizbPryGWJDidL1Fs02w27RYt3pH-QNP4qUZC~IqryLmsNoBMXPpst-eJ2S8rBypMkod2uzTJVrwhQtxonDLSMxLB5t~trmI5bwQw~Y2jZzUxACGKPq4U7WejHJ9oj2t1gUYEWYg65NCmTCcSfvgHmNn~XNnWJq5PGV35jRoHbKJJtymJMwGJ3i3oj4ikA1rDAUyNBQIkPyoDjc1RDuvCfLwjNEAWM5Mjc6kQx3NE2zCInQ2AyKYk7VLwRAzjsIfbPp9xzJLf01AUaKpsH~SoeqCPpbJv6ofMh-kXvD6dor39d72jO5WE6a6zPTkvEj4qMGjZwdzMDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Scalable_Algorithm_for_Tracking_an_Unknown_Number_of_Targets_Using_Multiple_Sensors","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":118990281,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990281/thumbnails/1.jpg","file_name":"1607.pdf","download_url":"https://www.academia.edu/attachments/118990281/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Scalable_Algorithm_for_Tracking_an_Unk.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990281/1607-libre.pdf?1729270581=\u0026response-content-disposition=attachment%3B+filename%3DA_Scalable_Algorithm_for_Tracking_an_Unk.pdf\u0026Expires=1732780710\u0026Signature=ShxEJr05tOIrizbPryGWJDidL1Fs02w27RYt3pH-QNP4qUZC~IqryLmsNoBMXPpst-eJ2S8rBypMkod2uzTJVrwhQtxonDLSMxLB5t~trmI5bwQw~Y2jZzUxACGKPq4U7WejHJ9oj2t1gUYEWYg65NCmTCcSfvgHmNn~XNnWJq5PGV35jRoHbKJJtymJMwGJ3i3oj4ikA1rDAUyNBQIkPyoDjc1RDuvCfLwjNEAWM5Mjc6kQx3NE2zCInQ2AyKYk7VLwRAzjsIfbPp9xzJLf01AUaKpsH~SoeqCPpbJv6ofMh-kXvD6dor39d72jO5WE6a6zPTkvEj4qMGjZwdzMDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability"},{"id":2537936,"name":"Factor Graph","url":"https://www.academia.edu/Documents/in/Factor_Graph"}],"urls":[{"id":45214429,"url":"http://xplorestaging.ieee.org/ielx7/78/7912413/07889057.pdf?arnumber=7889057"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528336"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528336/Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis"><img alt="Research paper thumbnail of Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/117172751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528336/Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis">Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis</a></div><div class="wp-workCard_item"><span>IEEE Open Journal of Signal Processing</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d3f68a1c20ecc5aa748d74b306ce19b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172751,"asset_id":122528336,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172751/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528336"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528336"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528336; 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Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say exp(−n I), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and the related error rate I depend on the given training set. The conditions for the exponential convergence can be verified and tested numerically exploiting the available dataset or a synthetic dataset generated according to the underlying statistical model. Coherently with the large deviations theory, we can also establish the convergence of the normalized D3F statistic to a Gaussian distribution. Furthermore, approximate error probability curves ζ n exp(−n I) are provided, thanks to the refined asymptotic derivation, where ζ n represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an accurate analytical approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations and by the use of real-world data, acquired by an X-band maritime radar system for surveillance.","publication_name":"IEEE Open Journal of Signal Processing","grobid_abstract_attachment_id":117172751},"translated_abstract":null,"internal_url":"https://www.academia.edu/122528336/Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis","translated_internal_url":"","created_at":"2024-08-02T04:19:39.103-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":117172751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172751/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172751/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172751/10008020-libre.pdf?1722600980=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=e-G5RbfdDElv8rnBAAjmHA5o5xmtKWuOx~PbRRvYOYsx6sBCNWkhmuBcUAShvDDfBubofPWmhdNARlbduJynXOLskBvuysR6BZcty83uyTYOzVm9Svrp5Bf~Pue~z4UT17nz9trevwynFk~mYek49827A0R6i7cMQyBDxMvI2hc-rsbuP~DxOJtFy1B2yRum3GybsyGYmqqLZ0JhvIKct2b3CsMhMUm41xGdCN0lkxSkcIa56khxaEG3ALJuoQahl3IM8X6Ky1ddeTaMCBkIhshbBbpd~aZ4dtXpt6H2pgv2rn2KDdxWAWJ9HZ2xNDrYcjBpnoVj1ARW3LMflVWxZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis","translated_slug":"","page_count":32,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":117172751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172751/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172751/download_file?st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172751/10008020-libre.pdf?1722600980=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=e-G5RbfdDElv8rnBAAjmHA5o5xmtKWuOx~PbRRvYOYsx6sBCNWkhmuBcUAShvDDfBubofPWmhdNARlbduJynXOLskBvuysR6BZcty83uyTYOzVm9Svrp5Bf~Pue~z4UT17nz9trevwynFk~mYek49827A0R6i7cMQyBDxMvI2hc-rsbuP~DxOJtFy1B2yRum3GybsyGYmqqLZ0JhvIKct2b3CsMhMUm41xGdCN0lkxSkcIa56khxaEG3ALJuoQahl3IM8X6Ky1ddeTaMCBkIhshbBbpd~aZ4dtXpt6H2pgv2rn2KDdxWAWJ9HZ2xNDrYcjBpnoVj1ARW3LMflVWxZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":117172754,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172754/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172754/download_file","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172754/10008020-libre.pdf?1722601585=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=Xb9mta8WTF-EEah1B1Rk4swhcs6m7zG7JKxm5u1YO~dOXk0aI4KSS725oup6yGQl~MdIbAooiPUjv7eVi~OEAMgbFTI9ciP90UCak5oiGN83WdlhD-7~SxrF1ZbB-xiUhHR1OxE1mJE17kaybYkubxd7q1d60F-kOLM3HOxfXuYUjHq4wB8aV5KIbRNOsOyni2vBQZ~x2D-HRTFgNL5iBTWMLJhS1X1SDic~6M5Nirij3wBU4g5qC8BYWg5ffLeKNNRyKnN7PBq4zHTPBrcn5b8Ua6oYlPImt1hxcVrWVHRY-OWNJIt1vfTV7gopBXytgbEeqHFOtOJPY7dYax35-Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":483393,"name":"Notation","url":"https://www.academia.edu/Documents/in/Notation"}],"urls":[{"id":43787500,"url":"http://xplorestaging.ieee.org/ielx7/8782710/9656695/10008020.pdf?arnumber=10008020"}]}, dispatcherData: dispatcherData }); 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However, in maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy. This paper extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can be tasked not only to predict but also to yield a corresponding prediction uncertainty via Bayesian modeling of epistemic and aleatoric uncertainties. We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved by using, if available, additional information on the intention of the ship (e.g., its planned destination).","publication_name":"IEEE Transactions on Aerospace and Electronic Systems","grobid_abstract_attachment_id":117172768},"translated_abstract":null,"internal_url":"https://www.academia.edu/122528335/Recurrent_Encoder_Decoder_Networks_for_Vessel_Trajectory_Prediction_With_Uncertainty_Estimation","translated_internal_url":"","created_at":"2024-08-02T04:19:38.907-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":117172768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172768/thumbnails/1.jpg","file_name":"2205.pdf","download_url":"https://www.academia.edu/attachments/117172768/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Recurrent_Encoder_Decoder_Networks_for_V.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172768/2205-libre.pdf?1722601589=\u0026response-content-disposition=attachment%3B+filename%3DRecurrent_Encoder_Decoder_Networks_for_V.pdf\u0026Expires=1732780711\u0026Signature=Z7nRatNxJHtWB1Kf4Q2HWerZ3ZtYxECVu7eWq6aY4TA~v70J4k61aK861T9EM10XBtjeTOBEvgo0kTKkVnYgLYGZ5rFg3mxwj3RJRKl1vErjlAEM4KYflxGVbxeu4j~KFIywm31xzOL9anQ0F2z7UobNZ56Au~XkKpMok0847oJqF5CRocIpw2XaZXeU9wRCAbgeYKaW9W5q5P9IvkTyWZoq-x8~dUndZsXOxqBZPkFY16UFHLfG1ZNYtPl1vMdr1Spx5WcVHi7np18VOfPXLpyDtZaGh3UtUZZ9Crz6ntRqVk9gWjRhuEXugypIJJuafXYqyOAhgSKFA0vxL7Ugwg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Recurrent_Encoder_Decoder_Networks_for_Vessel_Trajectory_Prediction_With_Uncertainty_Estimation","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":117172768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172768/thumbnails/1.jpg","file_name":"2205.pdf","download_url":"https://www.academia.edu/attachments/117172768/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Recurrent_Encoder_Decoder_Networks_for_V.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172768/2205-libre.pdf?1722601589=\u0026response-content-disposition=attachment%3B+filename%3DRecurrent_Encoder_Decoder_Networks_for_V.pdf\u0026Expires=1732780711\u0026Signature=Z7nRatNxJHtWB1Kf4Q2HWerZ3ZtYxECVu7eWq6aY4TA~v70J4k61aK861T9EM10XBtjeTOBEvgo0kTKkVnYgLYGZ5rFg3mxwj3RJRKl1vErjlAEM4KYflxGVbxeu4j~KFIywm31xzOL9anQ0F2z7UobNZ56Au~XkKpMok0847oJqF5CRocIpw2XaZXeU9wRCAbgeYKaW9W5q5P9IvkTyWZoq-x8~dUndZsXOxqBZPkFY16UFHLfG1ZNYtPl1vMdr1Spx5WcVHi7np18VOfPXLpyDtZaGh3UtUZZ9Crz6ntRqVk9gWjRhuEXugypIJJuafXYqyOAhgSKFA0vxL7Ugwg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":88,"name":"Aerospace Engineering","url":"https://www.academia.edu/Documents/in/Aerospace_Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":31477,"name":"Uncertainty Quantification","url":"https://www.academia.edu/Documents/in/Uncertainty_Quantification"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning"},{"id":140897,"name":"Encoder","url":"https://www.academia.edu/Documents/in/Encoder"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":1733865,"name":"Automatic Identification System","url":"https://www.academia.edu/Documents/in/Automatic_Identification_System"}],"urls":[{"id":43787499,"url":"http://xplorestaging.ieee.org/ielx7/7/10146528/09946391.pdf?arnumber=9946391"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528334/Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel"><img alt="Research paper thumbnail of Maritime Surveillance Using Spaceborne GNSS-Reflectometry: The Role of the Scattering Configuration and Receiving Polarization Channel" class="work-thumbnail" src="https://attachments.academia-assets.com/117172770/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528334/Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel">Maritime Surveillance Using Spaceborne GNSS-Reflectometry: The Role of the Scattering Configuration and Receiving Polarization Channel</a></div><div class="wp-workCard_item"><span>2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d262249a4c1282d6c2aebb050d26e5a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172770,"asset_id":122528334,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172770/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528334; 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In this paper, we provide a feasibility study of the ship detection problem using spaceborne GNSS-R data. The analysis is performed via the evaluation of the signal-to-noiseplus-clutter-ratio and signal-to-noise-ratio relevant to an isolated ship target in open sea. In particular, we investigated the impact of the GNSS-R acquisition geometry and radar signal polarization. The influence of sea state and ship orientation is assessed as well. The analysis is based on a sound theoretical electromagnetic model of the bistatic radar cross section of the ship target. The analysis clearly shows the benefits of 1) the backscattering configuration with respect to the conventional forward-scattering one and 2) the RHCP receiving channel w.r.t. the conventional LHCP one, used in sea surface analysis. However, the ship orientation and the sea state still play a key role in ship detectability.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)","grobid_abstract_attachment_id":117172770},"translated_abstract":null,"internal_url":"https://www.academia.edu/122528334/Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel","translated_internal_url":"","created_at":"2024-08-02T04:19:38.702-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":117172770,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172770/thumbnails/1.jpg","file_name":"RTSI_2018_GNSSR.pdf","download_url":"https://www.academia.edu/attachments/117172770/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Maritime_Surveillance_Using_Spaceborne_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172770/RTSI_2018_GNSSR-libre.pdf?1722600615=\u0026response-content-disposition=attachment%3B+filename%3DMaritime_Surveillance_Using_Spaceborne_G.pdf\u0026Expires=1732780711\u0026Signature=Sa193fph8eEnl8MidjDdk2f2kIN8tcWmUofE5-Ob4hOgDMfI44MxA48o9pO983hTQfdGZfYqLSOW~giyB8CNPCJ8dw3UzJm6oogz4URAHm7rSW4DvGWKePATQ0PhXeTNN19UJeNOLOFjm0UgWnGZw-qWdr5UnZ81ujBYl3Pdb~b-mg21tRdWdWBK3VVjfvlj3OS9jwgVMoHFczpZB1tqjOqwP8rIN7yssDFADnIXMnrlGSUgsRCGaSzLIiDQ93lDEcMxVcZWO~RE0-zm24AFM0jG1q0TCMWHTHlNK4yIEqvm4BB~JqcQznufNASv-yzdUHFejLWkygQjWhrGJrPNhw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":117172770,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172770/thumbnails/1.jpg","file_name":"RTSI_2018_GNSSR.pdf","download_url":"https://www.academia.edu/attachments/117172770/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Maritime_Surveillance_Using_Spaceborne_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172770/RTSI_2018_GNSSR-libre.pdf?1722600615=\u0026response-content-disposition=attachment%3B+filename%3DMaritime_Surveillance_Using_Spaceborne_G.pdf\u0026Expires=1732780711\u0026Signature=Sa193fph8eEnl8MidjDdk2f2kIN8tcWmUofE5-Ob4hOgDMfI44MxA48o9pO983hTQfdGZfYqLSOW~giyB8CNPCJ8dw3UzJm6oogz4URAHm7rSW4DvGWKePATQ0PhXeTNN19UJeNOLOFjm0UgWnGZw-qWdr5UnZ81ujBYl3Pdb~b-mg21tRdWdWBK3VVjfvlj3OS9jwgVMoHFczpZB1tqjOqwP8rIN7yssDFADnIXMnrlGSUgsRCGaSzLIiDQ93lDEcMxVcZWO~RE0-zm24AFM0jG1q0TCMWHTHlNK4yIEqvm4BB~JqcQznufNASv-yzdUHFejLWkygQjWhrGJrPNhw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":175926,"name":"GNSS applications","url":"https://www.academia.edu/Documents/in/GNSS_applications"},{"id":250384,"name":"Clutter","url":"https://www.academia.edu/Documents/in/Clutter"},{"id":499409,"name":"Reflectometry","url":"https://www.academia.edu/Documents/in/Reflectometry"},{"id":1458859,"name":"Bistatic Radar","url":"https://www.academia.edu/Documents/in/Bistatic_Radar"}],"urls":[{"id":43787498,"url":"http://xplorestaging.ieee.org/ielx7/8528317/8548348/08548373.pdf?arnumber=8548373"}]}, dispatcherData: dispatcherData }); 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I write this in March from the northeast US, which means that we've had to put away our February T-shirts and golf clubs in favor of parkas and snow shovels. Good (weather) luck to us all for the Spring! I hope to see many of you at the Radar Conference in Oklahoma City in April-the AESS Board of Governors has its spring meeting there, which I'm sure is an added attraction for many of you. It's my pleasure to introduce a full and interesting issue of contributed papers this month-we are getting so many excellent papers, both contributed and Special Issue (invited), that sometimes there is a delay to publication. We apologize that some spend time \"in the queue\". First, Wahl \u0026 Turkoglu from San Jose State have given us Nonlinear Receding Horizon Control-Based Real-Time Guidance, Navigation, and Control Architecture for Launch Vehicles. As the title promises, the article suggests an RHC strategy for space vehicle guidance, and in fact gives a nice historical context for the solution. The article is rather more mathematical than the typical magazine offering; but, well, control is mathematical. Huang \u0026 Lin from Aphelion Orbitals in Florida offer Fully Optical Spacecraft Communications: Implementing an Omnidirectional PV-Cell Receiver and 8 Mb/s LED Visible Light Downlink With Deep Learning Error Correction that suggests use of the extant on-board photovoltaic systems for the visible-light communication (VLC) uplinks. There are significant challenges, perhaps the greatest being the high raw error rate caused by low SNR and non-coherent demodulation; the authors propose a deep-learning decoding strategy. Coupled with a LED downlink this may be an economical product for upcoming cubesats. Waseem \u0026 Sadiq from the Satellite Research and Development Centre in Pakistan have given us Application of Model-Based Systems Engineering in Small Satellite Conceptual Design-A SysML Approach which is a nice systems engineering case study. The article describes the experience of use of SysML, which is a modeling language used to design and verify systems … and systems of systems. Since many of our members are concerned with systems engineering as a large part of their daily work, and since the small-satellite focus is emerging, we hope the reader will find this article timely and useful. And as air traffic control is indeed a large scale \"system,\" the theme is continued in Simulation Modelling of Traffic Collision Avoidance System With Wind Disturbance by Tang, Zhu \u0026 Fan, all from the National University of Defense Technology but with joint appointments at Barcelona and the Imperial College London. The application is air-traffic TCAS, and the focus is on the very necessary system description. A large team of authors from Airbus and the Universities of Valencia (Spain), Erlangen-Nurnberg (Germany) and Udine (Italy) have given us Multifunctional and Compact 3D FMCW MIMO Radar System With Rectangular Array for Medium-Range Applications. This is a particularly nice article that is exactly at the right technical level. Its title describes the subject, but only hints at the data fusion aspect: a camera also forms part of the system and aids considerably in tracking and classification. Further, this fusion facilitates change detection, which with its small size and economical design makes the system nicely suited for area protection or for UAV mounting. Finally, some words about the life of radar legend Philip Mayne Woodward, who passed from us on January 30 at age 98. Dr. Woodward's niece Suzette Woodward and colleague Susan Bond have kindly sent us their thoughts. I think most of us know of his contributions to radar, but his contributions to computer science (especially a language I remember fondly: Algol) and (this is surprising!) clock-making will be new at least to many of you. Thanks, Suzette!-Peter K. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528332/COVID_19_impact_on_global_maritime_mobility"><img alt="Research paper thumbnail of COVID-19 impact on global maritime mobility" class="work-thumbnail" src="https://attachments.academia-assets.com/117172750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528332/COVID_19_impact_on_global_maritime_mobility">COVID-19 impact on global maritime mobility</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world we...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. To highlight significant c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23514c0448d294ef0c4fda6f9dacb961" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172750,"asset_id":122528332,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172750/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528332]").text(description); $(".js-view-count[data-work-id=122528332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "23514c0448d294ef0c4fda6f9dacb961" } } $('.js-work-strip[data-work-id=122528332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528332,"title":"COVID-19 impact on global maritime mobility","translated_title":"","metadata":{"abstract":"To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. To highlight significant c...","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528331/A_Convenient_Analytical_Framework_for_Electromagnetic_Scattering_From_Composite_Targets"><img alt="Research paper thumbnail of A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets" class="work-thumbnail" src="https://attachments.academia-assets.com/117172761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528331/A_Convenient_Analytical_Framework_for_Electromagnetic_Scattering_From_Composite_Targets">A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets</a></div><div class="wp-workCard_item"><span>Radio Science</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper furnishes a convenient theoretical framework for the analytical evaluation of the bist...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. This is accomplished by addressing the following objectives: (1) to provide an explicit formulation of the scattering matrix under KA in terms of the incident and scattered unit wave vectors, (2) to provide a more generic derivation of the scattering matrix under the physical optics approximation by relaxing typical hypotheses regarding the geometry of the scattering problem, and (3) to highlight some important symmetries of the scatterin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cbd0abe171fa48b987b78bb17875fd80" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172761,"asset_id":122528331,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172761/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528331; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528331]").text(description); $(".js-view-count[data-work-id=122528331]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528331; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528331']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528331, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cbd0abe171fa48b987b78bb17875fd80" } } $('.js-work-strip[data-work-id=122528331]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528331,"title":"A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets","translated_title":"","metadata":{"abstract":"This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. This is accomplished by addressing the following objectives: (1) to provide an explicit formulation of the scattering matrix under KA in terms of the incident and scattered unit wave vectors, (2) to provide a more generic derivation of the scattering matrix under the physical optics approximation by relaxing typical hypotheses regarding the geometry of the scattering problem, and (3) to highlight some important symmetries of the scatterin...","publisher":"American Geophysical Union (AGU)","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Radio Science"},"translated_abstract":"This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. 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(ICASSP '03).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c498228af222b75f557f2ccb216d1bf3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172759,"asset_id":122528328,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172759/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528328]").text(description); $(".js-view-count[data-work-id=122528328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c498228af222b75f557f2ccb216d1bf3" } } $('.js-work-strip[data-work-id=122528328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528328,"title":"The VTP test for transients of equal detectability","translated_title":"","metadata":{"grobid_abstract":"For detection of a permanent and precisely-modeled change in distribution of iid observations, Page's test is optimal. 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We test the inferential capability of a recently developed beta-Bernoulli spatial scan statistic, which adds a beta prior to the original statistic. This pilot study, which includes two test scenarios with 6,000 data sets each, suggests a marked increase in power for a given false alert rate. We suggest a more extensive study would be worthwhile to corroborate the findings. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="4455743" id="journalpapers"><div class="js-work-strip profile--work_container" data-work-id="3809678"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/3809678/One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion"><img alt="Research paper thumbnail of One-bit Decentralized Detection with a Rao Test for Multisensor Fusion" class="work-thumbnail" src="https://attachments.academia-assets.com/31465115/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/3809678/One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion">One-bit Decentralized Detection with a Rao Test for Multisensor Fusion</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/WillettJohn">Peter Willett</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://unina.academia.edu/DomenicoCiuonzo">Domenico Ciuonzo</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this letter we propose the Rao test as a simpler alternative to the generalized likelihood rat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4eb3b0c10b92e526e2eb4dcb0e3941fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31465115,"asset_id":3809678,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31465115/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="3809678"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="3809678"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 3809678; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=3809678]").text(description); $(".js-view-count[data-work-id=3809678]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 3809678; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='3809678']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 3809678, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4eb3b0c10b92e526e2eb4dcb0e3941fa" } } $('.js-work-strip[data-work-id=3809678]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":3809678,"title":"One-bit Decentralized Detection with a Rao Test for Multisensor Fusion","translated_title":"","metadata":{"abstract":"In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. 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","internal_url":"https://www.academia.edu/3809678/One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion","translated_internal_url":"","created_at":"2013-06-28T02:44:42.407-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":977036,"coauthors_can_edit":true,"document_type":"other","co_author_tags":[{"id":13334062,"work_id":3809678,"tagging_user_id":977036,"tagged_user_id":41973045,"co_author_invite_id":596951,"email":"w***t@engr.uconn.edu","display_order":0,"name":"Peter Willett","title":"One-bit Decentralized Detection with a Rao Test for Multisensor Fusion"},{"id":13334162,"work_id":3809678,"tagging_user_id":977036,"tagged_user_id":null,"co_author_invite_id":3124119,"email":"g***a@studenti.unina2.it","display_order":4194304,"name":"Giuseppe Papa","title":"One-bit Decentralized Detection with a Rao Test for Multisensor Fusion"}],"downloadable_attachments":[{"id":31465115,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31465115/thumbnails/1.jpg","file_name":"1306.6141v1.pdf","download_url":"https://www.academia.edu/attachments/31465115/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"One_bit_Decentralized_Detection_with_a_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31465115/1306.6141v1-libre.pdf?1392301601=\u0026response-content-disposition=attachment%3B+filename%3DOne_bit_Decentralized_Detection_with_a_R.pdf\u0026Expires=1732780710\u0026Signature=S3czvJXYhZxXVseZYDJjPsqLm2KJuy09Fq7v2nZ95GLXWoINQrwEAk22AiRN5iCcl8A8nnBvH-e21W9RxYXCiUhXXAKI2RgYXiSZO3lWjx-Kwi~Z3utybFwYjYXsc1~qcJLEmPrine3wtSD0qEQsxGh9dZ95CfnRQWfuN788QLB2laim5B5xbNvcTI5I0KzJ2kMqsZbLRelH5H9nUNvhV1pSzwEUx6VoM1GaCMmaTkRrHOMAl8AEN3ohcCvBtPXuh5BvrmUv~Wt9~fIsZorvU0z9nuaiH5LbIogYx-8V~dZ1vbJind8TuBPtB8ej2bPb4w7ETbNUc3nymHgLukcBuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"One_bit_Decentralized_Detection_with_a_Rao_Test_for_Multisensor_Fusion","translated_slug":"","page_count":4,"language":"en","content_type":"Work","owner":{"id":977036,"first_name":"Domenico","middle_initials":null,"last_name":"Ciuonzo","page_name":"DomenicoCiuonzo","domain_name":"unina","created_at":"2011-11-23T19:29:36.482-08:00","display_name":"Domenico Ciuonzo","url":"https://unina.academia.edu/DomenicoCiuonzo"},"attachments":[{"id":31465115,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31465115/thumbnails/1.jpg","file_name":"1306.6141v1.pdf","download_url":"https://www.academia.edu/attachments/31465115/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"One_bit_Decentralized_Detection_with_a_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31465115/1306.6141v1-libre.pdf?1392301601=\u0026response-content-disposition=attachment%3B+filename%3DOne_bit_Decentralized_Detection_with_a_R.pdf\u0026Expires=1732780710\u0026Signature=S3czvJXYhZxXVseZYDJjPsqLm2KJuy09Fq7v2nZ95GLXWoINQrwEAk22AiRN5iCcl8A8nnBvH-e21W9RxYXCiUhXXAKI2RgYXiSZO3lWjx-Kwi~Z3utybFwYjYXsc1~qcJLEmPrine3wtSD0qEQsxGh9dZ95CfnRQWfuN788QLB2laim5B5xbNvcTI5I0KzJ2kMqsZbLRelH5H9nUNvhV1pSzwEUx6VoM1GaCMmaTkRrHOMAl8AEN3ohcCvBtPXuh5BvrmUv~Wt9~fIsZorvU0z9nuaiH5LbIogYx-8V~dZ1vbJind8TuBPtB8ej2bPb4w7ETbNUc3nymHgLukcBuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":5234,"name":"Detection \u0026 Estimation","url":"https://www.academia.edu/Documents/in/Detection_and_Estimation"},{"id":14852,"name":"Data Fusion (Engineering)","url":"https://www.academia.edu/Documents/in/Data_Fusion_Engineering_"},{"id":100379,"name":"Sensor Data Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Data_Fusion"},{"id":170496,"name":"Multi Sensor Data Fusion","url":"https://www.academia.edu/Documents/in/Multi_Sensor_Data_Fusion"},{"id":304533,"name":"Decision Fusion","url":"https://www.academia.edu/Documents/in/Decision_Fusion"},{"id":881836,"name":"GLRT","url":"https://www.academia.edu/Documents/in/GLRT"},{"id":881837,"name":"Rao test","url":"https://www.academia.edu/Documents/in/Rao_test"}],"urls":[{"id":1281731,"url":"http://arxiv.org/pdf/1306.6141v1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="5218261" id="papers"><div class="js-work-strip profile--work_container" data-work-id="124838196"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838196/An_extended_target_tracking_model_with_multiple_random_matrices_and_unified_kinematics"><img alt="Research paper thumbnail of An extended target tracking model with multiple random matrices and unified kinematics" class="work-thumbnail" src="https://attachments.academia-assets.com/118990277/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838196/An_extended_target_tracking_model_with_multiple_random_matrices_and_unified_kinematics">An extended target tracking model with multiple random matrices and unified kinematics</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 6, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="98c89bb8f83f0e952cc4e75060a26ace" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990277,"asset_id":124838196,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990277/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838196"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838196"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838196; 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class="js-work-strip profile--work_container" data-work-id="124838195"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods"><img alt="Research paper thumbnail of To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods" class="work-thumbnail" src="https://attachments.academia-assets.com/118990276/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods">To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 11, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f60303c55da7f48de7880892bdf692ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990276,"asset_id":124838195,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838195"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838195"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838195; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838195]").text(description); $(".js-view-count[data-work-id=124838195]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838195; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838195']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838195, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f60303c55da7f48de7880892bdf692ff" } } $('.js-work-strip[data-work-id=124838195]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838195,"title":"To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods","translated_title":"","metadata":{"publisher":"Cornell University","grobid_abstract":"Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.","publication_date":{"day":11,"month":8,"year":2023,"errors":{}},"publication_name":"arXiv (Cornell University)","grobid_abstract_attachment_id":118990276},"translated_abstract":null,"internal_url":"https://www.academia.edu/124838195/To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods","translated_internal_url":"","created_at":"2024-10-18T09:25:53.895-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118990276,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990276/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990276/2308-libre.pdf?1729270582=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=Tpizq0Fnil9Y5D9vD~aqjyZVa~GUUE6kn-aKGv2im4VWr3LcQ-tS9JWdAaKW8kErJpagfkP3YVqfWrTeKea8jwwf6GKmC-0oPyV3ZgW-vBV1Y~eoGc2Ns8fE2PbeYIgoDIjdtXLsqozz4IDKNTYaSfWD0rx88x~Z3Gr6xmIq3n48JvVtL0z~WHF9GM~vihi-lxFAb-NEdhP066eWgyVsEix9ccqtjud5n3OhcCPd9zAMyJDV2kE1NqxXTSjkgxfLreZ8KNsu9sRK6qxhBoHRoe~vRXEnxiBD-8nlyDGja5eQQZwQb2LbA~cB0bfPuqRb~GBB-OxSzoaXyhR~4sRKnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"To_Coalesce_or_to_Repel_An_Analysis_of_MHT_JPDA_and_Belief_Propagation_Multitarget_Tracking_Methods","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":118990276,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990276/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990276/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990276/2308-libre.pdf?1729270582=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=Tpizq0Fnil9Y5D9vD~aqjyZVa~GUUE6kn-aKGv2im4VWr3LcQ-tS9JWdAaKW8kErJpagfkP3YVqfWrTeKea8jwwf6GKmC-0oPyV3ZgW-vBV1Y~eoGc2Ns8fE2PbeYIgoDIjdtXLsqozz4IDKNTYaSfWD0rx88x~Z3Gr6xmIq3n48JvVtL0z~WHF9GM~vihi-lxFAb-NEdhP066eWgyVsEix9ccqtjud5n3OhcCPd9zAMyJDV2kE1NqxXTSjkgxfLreZ8KNsu9sRK6qxhBoHRoe~vRXEnxiBD-8nlyDGja5eQQZwQb2LbA~cB0bfPuqRb~GBB-OxSzoaXyhR~4sRKnA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":118990274,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990274/thumbnails/1.jpg","file_name":"2308.pdf","download_url":"https://www.academia.edu/attachments/118990274/download_file","bulk_download_file_name":"To_Coalesce_or_to_Repel_An_Analysis_of_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990274/2308-libre.pdf?1729270588=\u0026response-content-disposition=attachment%3B+filename%3DTo_Coalesce_or_to_Repel_An_Analysis_of_M.pdf\u0026Expires=1732780710\u0026Signature=N9q5wytYNugkUjxoawLuGHMs7EE4ag4Iy1eUthDvNfLKOS~udhsw4bdolB4aAYNInXFkbTCPzR7eppAQQcSR~Fbx7PYZS5nuX-aURjkL3JClHycS-fZPm~YN4NITpuKwH3gF4eC-lLpsknL4Wtz06fqkUFNpU8jwbTl-YAjnF6DtoFsviE6MwixUKdIfsCPRBpz6nCOFI89Ps0cdSQXu3IBuQL8KmmQqdMTkVRVcdxEcC2p9q79fr68pTM~QfRP4yrUr6tJH0blhapxxygsT5zBQrAOxlch1DLMUwNIgq~mtg1X~3m7doEEH2kXiGAtxddT7Kol8OXWENpwwtOTrDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":278182,"name":"Data Association","url":"https://www.academia.edu/Documents/in/Data_Association"},{"id":320537,"name":"Belief Propagation","url":"https://www.academia.edu/Documents/in/Belief_Propagation"}],"urls":[{"id":45214443,"url":"https://arxiv.org/pdf/2308.06326"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838194"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838194/3D_Localization_and_Tracking_Methods_for_Multi_Platform_Radar_Networks"><img alt="Research paper thumbnail of 3D Localization and Tracking Methods for Multi-Platform Radar Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/118990275/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838194/3D_Localization_and_Tracking_Methods_for_Multi_Platform_Radar_Networks">3D Localization and Tracking Methods for Multi-Platform Radar Networks</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 14, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdfb869ff9f883c41d767858c45a8830" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990275,"asset_id":124838194,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990275/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838194"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838194"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838194; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838194]").text(description); $(".js-view-count[data-work-id=124838194]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838194; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838194']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838194, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cdfb869ff9f883c41d767858c45a8830" } } $('.js-work-strip[data-work-id=124838194]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838194,"title":"3D Localization and Tracking Methods for Multi-Platform Radar Networks","translated_title":"","metadata":{"publisher":"Cornell University","ai_title_tag":"3D Localization and Tracking in Multi-Platform Radar Networks","grobid_abstract":"Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario.","publication_date":{"day":14,"month":8,"year":2023,"errors":{}},"publication_name":"arXiv (Cornell 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{"id":124838192,"title":"Highlights from the Sensor Array and Multichannel Technical Committee: Spotlight on the IEEE Signal Processing Society Technical Committees [In the Spotlight]","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers","publication_date":{"day":1,"month":9,"year":2018,"errors":{}},"publication_name":"IEEE Signal Processing 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src="https://attachments.academia-assets.com/118990268/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838190/Scalable_multitarget_tracking_using_multiple_sensors_A_belief_propagation_approach">Scalable multitarget tracking using multiple sensors: A belief propagation approach</a></div><div class="wp-workCard_item"><span>2015 18th International Conference on Information Fusion (Fusion)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a method for multisensor-multitarget tracking with excellent scalability in the number...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. Our method employs belief propagation based on a “detailed” factor graph that involves both target-related and measurement-related association variables. Using this approach, an increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. We observed very low runtimes of the proposed method; e.g., our MATLAB simulation of a scenario of 30 targets and 10 sensors without gating required less than one second per time step. The performance of the proposed method in terms of mean optimal subpattern assignment (OSPA) error compares well with that of state-of-the-art methods whose complexity scales exponentially with the number of targets. In particular, we observed that our method can outperfo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="097e6499b7db36a5e42ea03be7a69adc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990268,"asset_id":124838190,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990268/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838190"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838190]").text(description); $(".js-view-count[data-work-id=124838190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838190']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838190, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "097e6499b7db36a5e42ea03be7a69adc" } } $('.js-work-strip[data-work-id=124838190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838190,"title":"Scalable multitarget tracking using multiple sensors: A belief propagation approach","translated_title":"","metadata":{"abstract":"We propose a method for multisensor-multitarget tracking with excellent scalability in the number of targets (which is assumed known), the number of sensors, and the number of measurements per sensor. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838189/Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method"><img alt="Research paper thumbnail of Tracking an unknown number of targets using multiple sensors: A belief propagation method" class="work-thumbnail" src="https://attachments.academia-assets.com/118990266/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838189/Tracking_an_unknown_number_of_targets_using_multiple_sensors_A_belief_propagation_method">Tracking an unknown number of targets using multiple sensors: A belief propagation method</a></div><div class="wp-workCard_item"><span>2016 19th International Conference on Information Fusion (FUSION)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a multisensor method for tracking an unknown number of targets. Low computational comp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a multisensor method for tracking an unknown number of targets. Low computational complexity and very good scalability in the number of targets, number of sensors, and number of measurements per sensor are achieved by running a belief propagation (BP) message passing scheme on a suitably devised factor graph. Using a redundant formulation of data association uncertainty and “augmented target states” including target indicators allows the proposed BP method to leverage statistical independencies for a drastic reduction of complexity. The proposed method is shown to outperform previously proposed multisensor methods for multitarget tracking, including methods with a less favorable scaling behavior.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="936214ab20e689de8d3cec4b14324095" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990266,"asset_id":124838189,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990266/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124838189]").text(description); $(".js-view-count[data-work-id=124838189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124838189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124838189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124838189, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "936214ab20e689de8d3cec4b14324095" } } $('.js-work-strip[data-work-id=124838189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124838189,"title":"Tracking an unknown number of targets using multiple sensors: A belief propagation method","translated_title":"","metadata":{"abstract":"We propose a multisensor method for tracking an unknown number of targets. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="124838188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124838188/Signal_Amplitude_Estimation_and_Detection_From_Unlabeled_Binary_Quantized_Samples"><img alt="Research paper thumbnail of Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples" class="work-thumbnail" src="https://attachments.academia-assets.com/118990294/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124838188/Signal_Amplitude_Estimation_and_Detection_From_Unlabeled_Binary_Quantized_Samples">Signal Amplitude Estimation and Detection From Unlabeled Binary Quantized Samples</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Signal Processing</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="051c387bc2c26d01f096511410d02a59" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118990294,"asset_id":124838188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118990294/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124838188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124838188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124838188; 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First, maximum likelihood (ML) estimators are utilized to estimate both the permutation matrix and unknown signal amplitude under arbitrary, but known signal shape and quantizer thresholds. Sufficient conditions are provided under which an ML estimator can be found in polynomial time and an alternating maximization algorithm is proposed to solve the general problem via good initial estimates. In addition, the statistical identifiability of the model is studied. Furthermore, the generalized likelihood ratio test (GLRT) detector is adopted to detect the presence of signal. In addition, an accurate approximation to the probability of successful permutation matrix recovery is derived, and explicit expressions are provided to reveal the relationship between the number of signal samples and the number of quantizers. 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Our method achieves low computational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of \"augmented target states\" including binary target indicators make it possible to exploit statistical independencies for a drastic reduction of complexity. An increase in the number of targets, sensors, or measurements leads to additional variable nodes in the factor graph but not to higher dimensions of the messages. As a consequence, the complexity of our method scales only quadratically in the number of targets, linearly in the number of sensors, and linearly in the number of measurements per sensors. The performance of the method compares well with that of previously proposed methods, including methods with a less favorable scaling behavior. In particular, our method can outperform multisensor versions of the probability hypothesis density (PHD) filter, the cardinalized PHD filter, and the multi-Bernoulli filter.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"IEEE Transactions on Signal Processing","grobid_abstract_attachment_id":118990281},"translated_abstract":null,"internal_url":"https://www.academia.edu/124838171/A_Scalable_Algorithm_for_Tracking_an_Unknown_Number_of_Targets_Using_Multiple_Sensors","translated_internal_url":"","created_at":"2024-10-18T09:25:00.839-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118990281,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990281/thumbnails/1.jpg","file_name":"1607.pdf","download_url":"https://www.academia.edu/attachments/118990281/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Scalable_Algorithm_for_Tracking_an_Unk.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990281/1607-libre.pdf?1729270581=\u0026response-content-disposition=attachment%3B+filename%3DA_Scalable_Algorithm_for_Tracking_an_Unk.pdf\u0026Expires=1732780710\u0026Signature=ShxEJr05tOIrizbPryGWJDidL1Fs02w27RYt3pH-QNP4qUZC~IqryLmsNoBMXPpst-eJ2S8rBypMkod2uzTJVrwhQtxonDLSMxLB5t~trmI5bwQw~Y2jZzUxACGKPq4U7WejHJ9oj2t1gUYEWYg65NCmTCcSfvgHmNn~XNnWJq5PGV35jRoHbKJJtymJMwGJ3i3oj4ikA1rDAUyNBQIkPyoDjc1RDuvCfLwjNEAWM5Mjc6kQx3NE2zCInQ2AyKYk7VLwRAzjsIfbPp9xzJLf01AUaKpsH~SoeqCPpbJv6ofMh-kXvD6dor39d72jO5WE6a6zPTkvEj4qMGjZwdzMDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Scalable_Algorithm_for_Tracking_an_Unknown_Number_of_Targets_Using_Multiple_Sensors","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":118990281,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118990281/thumbnails/1.jpg","file_name":"1607.pdf","download_url":"https://www.academia.edu/attachments/118990281/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Scalable_Algorithm_for_Tracking_an_Unk.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118990281/1607-libre.pdf?1729270581=\u0026response-content-disposition=attachment%3B+filename%3DA_Scalable_Algorithm_for_Tracking_an_Unk.pdf\u0026Expires=1732780710\u0026Signature=ShxEJr05tOIrizbPryGWJDidL1Fs02w27RYt3pH-QNP4qUZC~IqryLmsNoBMXPpst-eJ2S8rBypMkod2uzTJVrwhQtxonDLSMxLB5t~trmI5bwQw~Y2jZzUxACGKPq4U7WejHJ9oj2t1gUYEWYg65NCmTCcSfvgHmNn~XNnWJq5PGV35jRoHbKJJtymJMwGJ3i3oj4ikA1rDAUyNBQIkPyoDjc1RDuvCfLwjNEAWM5Mjc6kQx3NE2zCInQ2AyKYk7VLwRAzjsIfbPp9xzJLf01AUaKpsH~SoeqCPpbJv6ofMh-kXvD6dor39d72jO5WE6a6zPTkvEj4qMGjZwdzMDQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability"},{"id":2537936,"name":"Factor Graph","url":"https://www.academia.edu/Documents/in/Factor_Graph"}],"urls":[{"id":45214429,"url":"http://xplorestaging.ieee.org/ielx7/78/7912413/07889057.pdf?arnumber=7889057"}]}, dispatcherData: dispatcherData }); 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Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say exp(−n I), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and the related error rate I depend on the given training set. The conditions for the exponential convergence can be verified and tested numerically exploiting the available dataset or a synthetic dataset generated according to the underlying statistical model. Coherently with the large deviations theory, we can also establish the convergence of the normalized D3F statistic to a Gaussian distribution. Furthermore, approximate error probability curves ζ n exp(−n I) are provided, thanks to the refined asymptotic derivation, where ζ n represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an accurate analytical approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations and by the use of real-world data, acquired by an X-band maritime radar system for surveillance.","publication_name":"IEEE Open Journal of Signal Processing","grobid_abstract_attachment_id":117172751},"translated_abstract":null,"internal_url":"https://www.academia.edu/122528336/Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis","translated_internal_url":"","created_at":"2024-08-02T04:19:39.103-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":117172751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172751/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172751/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172751/10008020-libre.pdf?1722600980=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=e-G5RbfdDElv8rnBAAjmHA5o5xmtKWuOx~PbRRvYOYsx6sBCNWkhmuBcUAShvDDfBubofPWmhdNARlbduJynXOLskBvuysR6BZcty83uyTYOzVm9Svrp5Bf~Pue~z4UT17nz9trevwynFk~mYek49827A0R6i7cMQyBDxMvI2hc-rsbuP~DxOJtFy1B2yRum3GybsyGYmqqLZ0JhvIKct2b3CsMhMUm41xGdCN0lkxSkcIa56khxaEG3ALJuoQahl3IM8X6Ky1ddeTaMCBkIhshbBbpd~aZ4dtXpt6H2pgv2rn2KDdxWAWJ9HZ2xNDrYcjBpnoVj1ARW3LMflVWxZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Statistical_Hypothesis_Testing_Based_on_Machine_Learning_Large_Deviations_Analysis","translated_slug":"","page_count":32,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":117172751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172751/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172751/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172751/10008020-libre.pdf?1722600980=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=e-G5RbfdDElv8rnBAAjmHA5o5xmtKWuOx~PbRRvYOYsx6sBCNWkhmuBcUAShvDDfBubofPWmhdNARlbduJynXOLskBvuysR6BZcty83uyTYOzVm9Svrp5Bf~Pue~z4UT17nz9trevwynFk~mYek49827A0R6i7cMQyBDxMvI2hc-rsbuP~DxOJtFy1B2yRum3GybsyGYmqqLZ0JhvIKct2b3CsMhMUm41xGdCN0lkxSkcIa56khxaEG3ALJuoQahl3IM8X6Ky1ddeTaMCBkIhshbBbpd~aZ4dtXpt6H2pgv2rn2KDdxWAWJ9HZ2xNDrYcjBpnoVj1ARW3LMflVWxZg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":117172754,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172754/thumbnails/1.jpg","file_name":"10008020.pdf","download_url":"https://www.academia.edu/attachments/117172754/download_file","bulk_download_file_name":"Statistical_Hypothesis_Testing_Based_on.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172754/10008020-libre.pdf?1722601585=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Hypothesis_Testing_Based_on.pdf\u0026Expires=1732780710\u0026Signature=Xb9mta8WTF-EEah1B1Rk4swhcs6m7zG7JKxm5u1YO~dOXk0aI4KSS725oup6yGQl~MdIbAooiPUjv7eVi~OEAMgbFTI9ciP90UCak5oiGN83WdlhD-7~SxrF1ZbB-xiUhHR1OxE1mJE17kaybYkubxd7q1d60F-kOLM3HOxfXuYUjHq4wB8aV5KIbRNOsOyni2vBQZ~x2D-HRTFgNL5iBTWMLJhS1X1SDic~6M5Nirij3wBU4g5qC8BYWg5ffLeKNNRyKnN7PBq4zHTPBrcn5b8Ua6oYlPImt1hxcVrWVHRY-OWNJIt1vfTV7gopBXytgbEeqHFOtOJPY7dYax35-Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":483393,"name":"Notation","url":"https://www.academia.edu/Documents/in/Notation"}],"urls":[{"id":43787500,"url":"http://xplorestaging.ieee.org/ielx7/8782710/9656695/10008020.pdf?arnumber=10008020"}]}, dispatcherData: dispatcherData }); 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However, in maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy. This paper extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can be tasked not only to predict but also to yield a corresponding prediction uncertainty via Bayesian modeling of epistemic and aleatoric uncertainties. We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved by using, if available, additional information on the intention of the ship (e.g., its planned destination).","publication_name":"IEEE Transactions on Aerospace and Electronic Systems","grobid_abstract_attachment_id":117172768},"translated_abstract":null,"internal_url":"https://www.academia.edu/122528335/Recurrent_Encoder_Decoder_Networks_for_Vessel_Trajectory_Prediction_With_Uncertainty_Estimation","translated_internal_url":"","created_at":"2024-08-02T04:19:38.907-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":41973045,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":117172768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172768/thumbnails/1.jpg","file_name":"2205.pdf","download_url":"https://www.academia.edu/attachments/117172768/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Recurrent_Encoder_Decoder_Networks_for_V.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172768/2205-libre.pdf?1722601589=\u0026response-content-disposition=attachment%3B+filename%3DRecurrent_Encoder_Decoder_Networks_for_V.pdf\u0026Expires=1732780711\u0026Signature=Z7nRatNxJHtWB1Kf4Q2HWerZ3ZtYxECVu7eWq6aY4TA~v70J4k61aK861T9EM10XBtjeTOBEvgo0kTKkVnYgLYGZ5rFg3mxwj3RJRKl1vErjlAEM4KYflxGVbxeu4j~KFIywm31xzOL9anQ0F2z7UobNZ56Au~XkKpMok0847oJqF5CRocIpw2XaZXeU9wRCAbgeYKaW9W5q5P9IvkTyWZoq-x8~dUndZsXOxqBZPkFY16UFHLfG1ZNYtPl1vMdr1Spx5WcVHi7np18VOfPXLpyDtZaGh3UtUZZ9Crz6ntRqVk9gWjRhuEXugypIJJuafXYqyOAhgSKFA0vxL7Ugwg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Recurrent_Encoder_Decoder_Networks_for_Vessel_Trajectory_Prediction_With_Uncertainty_Estimation","translated_slug":"","page_count":10,"language":"en","content_type":"Work","owner":{"id":41973045,"first_name":"Peter","middle_initials":"","last_name":"Willett","page_name":"WillettJohn","domain_name":"independent","created_at":"2016-01-23T14:42:17.112-08:00","display_name":"Peter Willett","url":"https://independent.academia.edu/WillettJohn"},"attachments":[{"id":117172768,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/117172768/thumbnails/1.jpg","file_name":"2205.pdf","download_url":"https://www.academia.edu/attachments/117172768/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Recurrent_Encoder_Decoder_Networks_for_V.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/117172768/2205-libre.pdf?1722601589=\u0026response-content-disposition=attachment%3B+filename%3DRecurrent_Encoder_Decoder_Networks_for_V.pdf\u0026Expires=1732780711\u0026Signature=Z7nRatNxJHtWB1Kf4Q2HWerZ3ZtYxECVu7eWq6aY4TA~v70J4k61aK861T9EM10XBtjeTOBEvgo0kTKkVnYgLYGZ5rFg3mxwj3RJRKl1vErjlAEM4KYflxGVbxeu4j~KFIywm31xzOL9anQ0F2z7UobNZ56Au~XkKpMok0847oJqF5CRocIpw2XaZXeU9wRCAbgeYKaW9W5q5P9IvkTyWZoq-x8~dUndZsXOxqBZPkFY16UFHLfG1ZNYtPl1vMdr1Spx5WcVHi7np18VOfPXLpyDtZaGh3UtUZZ9Crz6ntRqVk9gWjRhuEXugypIJJuafXYqyOAhgSKFA0vxL7Ugwg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":88,"name":"Aerospace Engineering","url":"https://www.academia.edu/Documents/in/Aerospace_Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":31477,"name":"Uncertainty Quantification","url":"https://www.academia.edu/Documents/in/Uncertainty_Quantification"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":81182,"name":"Deep Learning","url":"https://www.academia.edu/Documents/in/Deep_Learning"},{"id":140897,"name":"Encoder","url":"https://www.academia.edu/Documents/in/Encoder"},{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"},{"id":1733865,"name":"Automatic Identification System","url":"https://www.academia.edu/Documents/in/Automatic_Identification_System"}],"urls":[{"id":43787499,"url":"http://xplorestaging.ieee.org/ielx7/7/10146528/09946391.pdf?arnumber=9946391"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528334/Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel"><img alt="Research paper thumbnail of Maritime Surveillance Using Spaceborne GNSS-Reflectometry: The Role of the Scattering Configuration and Receiving Polarization Channel" class="work-thumbnail" src="https://attachments.academia-assets.com/117172770/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528334/Maritime_Surveillance_Using_Spaceborne_GNSS_Reflectometry_The_Role_of_the_Scattering_Configuration_and_Receiving_Polarization_Channel">Maritime Surveillance Using Spaceborne GNSS-Reflectometry: The Role of the Scattering Configuration and Receiving Polarization Channel</a></div><div class="wp-workCard_item"><span>2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d262249a4c1282d6c2aebb050d26e5a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172770,"asset_id":122528334,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172770/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528334; 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In this paper, we provide a feasibility study of the ship detection problem using spaceborne GNSS-R data. The analysis is performed via the evaluation of the signal-to-noiseplus-clutter-ratio and signal-to-noise-ratio relevant to an isolated ship target in open sea. In particular, we investigated the impact of the GNSS-R acquisition geometry and radar signal polarization. The influence of sea state and ship orientation is assessed as well. The analysis is based on a sound theoretical electromagnetic model of the bistatic radar cross section of the ship target. The analysis clearly shows the benefits of 1) the backscattering configuration with respect to the conventional forward-scattering one and 2) the RHCP receiving channel w.r.t. the conventional LHCP one, used in sea surface analysis. 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I write this in March from the northeast US, which means that we've had to put away our February T-shirts and golf clubs in favor of parkas and snow shovels. Good (weather) luck to us all for the Spring! I hope to see many of you at the Radar Conference in Oklahoma City in April-the AESS Board of Governors has its spring meeting there, which I'm sure is an added attraction for many of you. It's my pleasure to introduce a full and interesting issue of contributed papers this month-we are getting so many excellent papers, both contributed and Special Issue (invited), that sometimes there is a delay to publication. We apologize that some spend time \"in the queue\". First, Wahl \u0026 Turkoglu from San Jose State have given us Nonlinear Receding Horizon Control-Based Real-Time Guidance, Navigation, and Control Architecture for Launch Vehicles. As the title promises, the article suggests an RHC strategy for space vehicle guidance, and in fact gives a nice historical context for the solution. The article is rather more mathematical than the typical magazine offering; but, well, control is mathematical. Huang \u0026 Lin from Aphelion Orbitals in Florida offer Fully Optical Spacecraft Communications: Implementing an Omnidirectional PV-Cell Receiver and 8 Mb/s LED Visible Light Downlink With Deep Learning Error Correction that suggests use of the extant on-board photovoltaic systems for the visible-light communication (VLC) uplinks. There are significant challenges, perhaps the greatest being the high raw error rate caused by low SNR and non-coherent demodulation; the authors propose a deep-learning decoding strategy. Coupled with a LED downlink this may be an economical product for upcoming cubesats. Waseem \u0026 Sadiq from the Satellite Research and Development Centre in Pakistan have given us Application of Model-Based Systems Engineering in Small Satellite Conceptual Design-A SysML Approach which is a nice systems engineering case study. The article describes the experience of use of SysML, which is a modeling language used to design and verify systems … and systems of systems. Since many of our members are concerned with systems engineering as a large part of their daily work, and since the small-satellite focus is emerging, we hope the reader will find this article timely and useful. And as air traffic control is indeed a large scale \"system,\" the theme is continued in Simulation Modelling of Traffic Collision Avoidance System With Wind Disturbance by Tang, Zhu \u0026 Fan, all from the National University of Defense Technology but with joint appointments at Barcelona and the Imperial College London. The application is air-traffic TCAS, and the focus is on the very necessary system description. A large team of authors from Airbus and the Universities of Valencia (Spain), Erlangen-Nurnberg (Germany) and Udine (Italy) have given us Multifunctional and Compact 3D FMCW MIMO Radar System With Rectangular Array for Medium-Range Applications. This is a particularly nice article that is exactly at the right technical level. Its title describes the subject, but only hints at the data fusion aspect: a camera also forms part of the system and aids considerably in tracking and classification. Further, this fusion facilitates change detection, which with its small size and economical design makes the system nicely suited for area protection or for UAV mounting. Finally, some words about the life of radar legend Philip Mayne Woodward, who passed from us on January 30 at age 98. Dr. Woodward's niece Suzette Woodward and colleague Susan Bond have kindly sent us their thoughts. I think most of us know of his contributions to radar, but his contributions to computer science (especially a language I remember fondly: Algol) and (this is surprising!) clock-making will be new at least to many of you. Thanks, Suzette!-Peter K. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528332/COVID_19_impact_on_global_maritime_mobility"><img alt="Research paper thumbnail of COVID-19 impact on global maritime mobility" class="work-thumbnail" src="https://attachments.academia-assets.com/117172750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528332/COVID_19_impact_on_global_maritime_mobility">COVID-19 impact on global maritime mobility</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world we...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. To highlight significant c...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23514c0448d294ef0c4fda6f9dacb961" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172750,"asset_id":122528332,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172750/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528332]").text(description); $(".js-view-count[data-work-id=122528332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "23514c0448d294ef0c4fda6f9dacb961" } } $('.js-work-strip[data-work-id=122528332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528332,"title":"COVID-19 impact on global maritime mobility","translated_title":"","metadata":{"abstract":"To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. To highlight significant c...","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number ofactiveandidleships, and fleet average speed. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="122528331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/122528331/A_Convenient_Analytical_Framework_for_Electromagnetic_Scattering_From_Composite_Targets"><img alt="Research paper thumbnail of A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets" class="work-thumbnail" src="https://attachments.academia-assets.com/117172761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/122528331/A_Convenient_Analytical_Framework_for_Electromagnetic_Scattering_From_Composite_Targets">A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets</a></div><div class="wp-workCard_item"><span>Radio Science</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper furnishes a convenient theoretical framework for the analytical evaluation of the bist...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. This is accomplished by addressing the following objectives: (1) to provide an explicit formulation of the scattering matrix under KA in terms of the incident and scattered unit wave vectors, (2) to provide a more generic derivation of the scattering matrix under the physical optics approximation by relaxing typical hypotheses regarding the geometry of the scattering problem, and (3) to highlight some important symmetries of the scatterin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cbd0abe171fa48b987b78bb17875fd80" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172761,"asset_id":122528331,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172761/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528331; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528331]").text(description); $(".js-view-count[data-work-id=122528331]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528331; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528331']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528331, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cbd0abe171fa48b987b78bb17875fd80" } } $('.js-work-strip[data-work-id=122528331]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528331,"title":"A Convenient Analytical Framework for Electromagnetic Scattering From Composite Targets","translated_title":"","metadata":{"abstract":"This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. This is accomplished by addressing the following objectives: (1) to provide an explicit formulation of the scattering matrix under KA in terms of the incident and scattered unit wave vectors, (2) to provide a more generic derivation of the scattering matrix under the physical optics approximation by relaxing typical hypotheses regarding the geometry of the scattering problem, and (3) to highlight some important symmetries of the scatterin...","publisher":"American Geophysical Union (AGU)","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Radio Science"},"translated_abstract":"This paper furnishes a convenient theoretical framework for the analytical evaluation of the bistatic scattering coefficients, under the Kirchhoff approximation (KA) in electromagnetics. Starting from the KA, specific results under the geometrical optics and physical optics approximations are furnished, along with the backscattering geometry. The main aim is to provide closed‐form expressions of the scattering matrix that are suited to scenarios where multiple‐bounce scattering comes into play and/or surfaces with arbitrary unit normal are present. 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(ICASSP '03).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c498228af222b75f557f2ccb216d1bf3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117172759,"asset_id":122528328,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117172759/download_file?st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&st=MTczMjc3NzExMSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="122528328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="122528328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 122528328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=122528328]").text(description); $(".js-view-count[data-work-id=122528328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 122528328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='122528328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 122528328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c498228af222b75f557f2ccb216d1bf3" } } $('.js-work-strip[data-work-id=122528328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":122528328,"title":"The VTP test for transients of equal detectability","translated_title":"","metadata":{"grobid_abstract":"For detection of a permanent and precisely-modeled change in distribution of iid observations, Page's test is optimal. 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