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class="profile--tab_heading_container js-section-heading" data-section="Publications" id="Publications"><h3 class="profile--tab_heading_container">Publications by Aboelmagd Noureldin</h3></div><div class="js-work-strip profile--work_container" data-work-id="12282810"><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/12282810/Method_and_System_for_Estimating_Multiple_Modes_of_Motion"><img alt="Research paper thumbnail of Method and System for Estimating Multiple Modes of Motion" class="work-thumbnail" src="https://attachments.academia-assets.com/37867266/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/12282810/Method_and_System_for_Estimating_Multiple_Modes_of_Motion">Method and System for Estimating Multiple Modes of Motion</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://shams.academia.edu/MostafaElhoushi">Mostafa Elhoushi</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AboelmagdNoureldin">Aboelmagd Noureldin</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A method and system for determining the mode of motion or conveyance of a device, the device bein...</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">A method and system for determining the mode of motion or conveyance of a device, the device being within a platform (e.g., a person, vehicle, or vessel of any type). The device can be strapped or non-strapped to the platform, and where non-strapped, the mobility of the device may be constrained or unconstrained within the platform and the device may be moved or tilted to any orientation within the platform, without degradation in performance of determining the mode of motion. This method can utilize measurements (readings) from sensors in the device (such as for example, accelerometers, gyroscopes, etc.) whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning). The present method and system may be used in any one or both of two different phases, a model building phase or a model utilization phase.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0c56086d3caff7f4a96a88461f292430" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37867266,"asset_id":12282810,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37867266/download_file?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="12282810"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12282810"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12282810; 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$(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="7752302"><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/7752302/Using_Portable_Device_Sensors_to_Recognize_Height_Changing_Modes_of_Motion"><img alt="Research paper thumbnail of Using Portable Device Sensors to Recognize Height Changing Modes of Motion" class="work-thumbnail" src="https://attachments.academia-assets.com/34269344/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/7752302/Using_Portable_Device_Sensors_to_Recognize_Height_Changing_Modes_of_Motion">Using Portable Device Sensors to Recognize Height Changing Modes of Motion</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/AboelmagdNoureldin">Aboelmagd Noureldin</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://shams.academia.edu/MostafaElhoushi">Mostafa Elhoushi</a></span></div><div class="wp-workCard_item"><span>Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International</span><span>, May 13, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In portable navigation, the need to recognize the motion mode of the user, is useful - if not nec...</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 portable navigation, the need to recognize the motion mode of the user, is useful - if not necessary - to improve the positioning estimation. This paper explains the use of sensors in a portable device, such as a smartphone, to recognize the user's mode of motion when a change in height is detected. The modes of motion detected are walking up or down stairs, taking the elevator, and standing or walking on an escalator. The portable device contains an accelerometer triad, gyroscope triad, a barometer, and occasionally a magnetometer triad. The solution is dependent on the sensors, and does not require satellite or wireless positioning. The height motion mode recognition module has been implemented in real-time on several brands of various consumer portable devices, including smartphones, tablets, smartwatches, and smart glasses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34f9d593abc6345e94966507713dffe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34269344,"asset_id":7752302,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34269344/download_file?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="7752302"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7752302"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7752302; 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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="7673806"><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/7673806/Robust_Motion_Mode_Recognition_for_Portable_Navigation_Independent_on_Device_Usage"><img alt="Research paper thumbnail of Robust Motion Mode Recognition for Portable Navigation Independent on Device Usage" class="work-thumbnail" src="https://attachments.academia-assets.com/34207749/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/7673806/Robust_Motion_Mode_Recognition_for_Portable_Navigation_Independent_on_Device_Usage">Robust Motion Mode Recognition for Portable Navigation Independent on Device Usage</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://shams.academia.edu/MostafaElhoushi">Mostafa Elhoushi</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AboelmagdNoureldin">Aboelmagd Noureldin</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Portable navigation has become increasingly prevalent in daily activities. The need for accurate ...</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">Portable navigation has become increasingly prevalent in daily activities. The need for accurate user positioning information, including a person's location and velocity, when using a portable device (such as a cell phone, tablet, or even a smart watch) is growing in various fields. Knowing the user's mode of motion or conveyance allows appropriate algorithms or constraints, related to each mode, to be used to estimate a more accurate position and velocity. The modes covered in this paper are walking, running, cycling, and land-based vessels (including vehicles, truck, buses, and trains which include light rail trains and subways). The work discussed in this paper involves the use of sensors — with and without Global Navigation Satellite Systems (GNSS) signal availability —in portable devices to help recognize the mode of motion for an arbitrary user, an arbitrary use case — whether the device is held in the hand, in the pocket, or at the ear, etc. — and an arbitrary orientation of the device.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="89295804bacab71081869d25635778bc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34207749,"asset_id":7673806,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34207749/download_file?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="7673806"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7673806"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7673806; 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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 Aboelmagd Noureldin</h3></div><div class="js-work-strip profile--work_container" data-work-id="111447446"><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/111447446/Low_Cost_Real_Time_PPP_INS_Integration_for_Automated_Land_Vehicles"><img alt="Research paper thumbnail of Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles" class="work-thumbnail" src="https://attachments.academia-assets.com/108985138/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/111447446/Low_Cost_Real_Time_PPP_INS_Integration_for_Automated_Land_Vehicles">Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The last decade has witnessed a growing demand for precise positioning in many applications inclu...</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">The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the develop...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ce64f046ac96e2e6e710c73d16da43fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108985138,"asset_id":111447446,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108985138/download_file?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="111447446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111447446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111447446; 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In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers&#39; measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer&#39;s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors....</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584457"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584457"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584457; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584457]").text(description); $(".js-view-count[data-work-id=16584457]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584457; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584457']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584457]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584457,"title":"An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors","internal_url":"https://www.academia.edu/16584457/An_Enhanced_Error_Model_for_EKF_Based_Tightly_Coupled_Integration_of_GPS_and_Land_Vehicles_Motion_Sensors","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584454"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system"><img alt="Research paper thumbnail of Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system">Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Author: R. Sharaf, M. Tarbouchi, A. El-Shafie and A. Noureldin. Meeting: Proceedings of the 2...</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">... Author: R. Sharaf, M. Tarbouchi, A. El-Shafie and A. Noureldin. Meeting: Proceedings of the 2005 National Technical Meeting of The Institute of Navigation January 24 - 26, 2005 The Catamaran Resort Hotel San Diego, CA. Page(s): 235 - 242. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584454"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584454"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584454; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584454]").text(description); $(".js-view-count[data-work-id=16584454]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584454; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584454']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584454]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584454,"title":"Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system","internal_url":"https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584451"><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/16584451/Dynamic_Online_Calibrated_Radio_Maps_for_Indoor_Positioning_in_Wireless_Local_Area_Networks"><img alt="Research paper thumbnail of Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/40036599/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/16584451/Dynamic_Online_Calibrated_Radio_Maps_for_Indoor_Positioning_in_Wireless_Local_Area_Networks">Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Context-awareness and Location-Based-Services are of great importance in mobile computing environ...</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">Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c6b7a734215a96e9ec9bf673c2f06180" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036599,"asset_id":16584451,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036599/download_file?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="16584451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584451; 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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="16584450"><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/16584450/Real_time_Cycle_slip_Detection_and_Correction_for_Land_Vehicle_Navigation_Using_Inertial_Aiding"><img alt="Research paper thumbnail of Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation Using Inertial Aiding" class="work-thumbnail" src="https://attachments.academia-assets.com/40036598/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/16584450/Real_time_Cycle_slip_Detection_and_Correction_for_Land_Vehicle_Navigation_Using_Inertial_Aiding">Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation Using Inertial Aiding</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Carrier phase measurements require resolution of integer ambiguities before precise positioning c...</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">Carrier phase measurements require resolution of integer ambiguities before precise positioning can be achieved. The GPS receiver can keep track of the integer number of cycles as long as the receiver maintains lock to the satellite signal. However, in reality, the GPS signal could be interrupted momentary by some disturbing factors leading to a discontinuity of an integer number of cycles in the measured carrier phase. This interruption in the counting of cycles in the carrier phase measurements is known as a cycle slip. After a cycle slip, ambiguities need to be resolved again or cycle slips need to be corrected to resume the precise positioning/navigation process. These cycle slips can, to some extent, be detected and fixed to avoid delay and computation complexity attributed to the process of integer ambiguity resolution. Capitalizing on the complementary characteristics of INS and GPS, INS is used to provide external information to detect and correct cycle slips. Lately, MEMS grade inertial sensors are being used for low cost navigation applications. Moreover, recent research is geared towards the use of fewer numbers of sensors avoiding their complex errors and reducing the cost. This paper introduces integration of GPS and reduced inertial sensor system (RISS) to address the problem of cycle slips. The performance of proposed method is examined on several real-life land vehicle trajectories which included various challenging driving scenarios including high and slow speeds, sudden accelerations and decelerations, sharp turns and steep slopes etc. Results demonstrate the effectiveness of the proposed algorithm in these severe conditions which cause intensive and variable-sized cycle slips. This research has a direct influence on navigation in challenging environments where frequent cycle slips occur and resolving integer ambiguities is not affordable because of time and computational constraints. An additional consequence of this research is the significant reduction in the cost of an integrated system due to the use of fewer MEMS inertial sensors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03e92c91ae1ae5691a245e3fc6834e86" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036598,"asset_id":16584450,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036598/download_file?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="16584450"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584450"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584450; 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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="16584448"><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/16584448/An_Adaptive_Positioning_System_for_Smartphones_in_Zigbee_Networks_Using_Channel_Decomposition_and_Particle_Swarm_Optimization"><img alt="Research paper thumbnail of An Adaptive Positioning System for Smartphones in Zigbee Networks Using Channel Decomposition and Particle Swarm Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/40036575/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/16584448/An_Adaptive_Positioning_System_for_Smartphones_in_Zigbee_Networks_Using_Channel_Decomposition_and_Particle_Swarm_Optimization">An Adaptive Positioning System for Smartphones in Zigbee Networks Using Channel Decomposition and Particle Swarm Optimization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ZigBee wireless sensor networks has the benefit of superior optimization in power consumption and...</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">ZigBee wireless sensor networks has the benefit of superior optimization in power consumption and extremely long battery life. In near future, the handheld devices are expected to incorporate support for wireless sensor networks technology, and these devices can become active elements in ZigBee-based applications such as automated buildings and advanced metering systems. Use of these applications is also promising for the expansion of location services into environments that do not have access to Global Navigation Satellite Systems (GNSS). Compared with the ToA (Time of Arrival) and TDoA (Time difference of Arrival) methods, RSSI (Received Signal Strength Indicator) has several advantages. It does not require additional hardware. Owing to the simplicity of RSSI, the most recent studies on localization using wireless sensor networks use RSSI-based algorithms. Previous works RSSI-based location estimation methods discussed by the literature depend on the impractical assumption that si...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b090cb0332d60617432aaf1ca6b51394" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036575,"asset_id":16584448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036575/download_file?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="16584448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584448; 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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="16584447"><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/16584447/Enhancing_Vision_Aided_GNSS_INS_Navigation_Systems_Using_Nonlinear_Modeling_Techniques_Based_on_Fast_Orthogonal_Search_with_Double_Filtering_Mechanism"><img alt="Research paper thumbnail of Enhancing Vision-Aided GNSS/INS Navigation Systems Using Nonlinear Modeling Techniques Based on Fast Orthogonal Search with Double-Filtering Mechanism" class="work-thumbnail" src="https://attachments.academia-assets.com/40036597/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/16584447/Enhancing_Vision_Aided_GNSS_INS_Navigation_Systems_Using_Nonlinear_Modeling_Techniques_Based_on_Fast_Orthogonal_Search_with_Double_Filtering_Mechanism">Enhancing Vision-Aided GNSS/INS Navigation Systems Using Nonlinear Modeling Techniques Based on Fast Orthogonal Search with Double-Filtering Mechanism</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In vision-aided INS/GNSS integrated navigation systems, several errors contribute to significant ...</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 vision-aided INS/GNSS integrated navigation systems, several errors contribute to significant drifts in the absence of GNSS and during severe GNSS interruptions. These errors includes cameras distortion, camera calibration, camera misalignments, and feature extraction/tracking errors. In addition, inertial sensors biases, drifts, non-orthogonality, non-normality and misalignment also significantly affect the dead-reckoning navigation accuracy in the absence of GNSS. Due to the complex nonlinearity inherent in these errors, this paper introduces a double-filter mechanism enhanced by nonlinear error modeling for multi-sensor vision-aided integrated navigation systems for land vehicles. The proposed system integrates GNSS, vision, INS, and odometry. The proposed methodology tries to reduce the effects of nonlinear errors that propagate through different components of the integrated navigation system. The proposed methodology can work in real-time due to the utilization of a fast ort...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad46a90ebddbd5872c186200182835a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036597,"asset_id":16584447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036597/download_file?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="16584447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584447]").text(description); $(".js-view-count[data-work-id=16584447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="16584446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers"><img alt="Research paper thumbnail of High Resolution Fine Acquisition Algorithm for GNSS Receivers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers">High Resolution Fine Acquisition Algorithm for GNSS Receivers</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse...</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">The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse estimate of the GPS signal Doppler frequency shift and code delay for use by the tracking loops. The accuracy of the signal acquisition has a direct influence on the tracking performance. The implementation of a GPS signal acquisition algorithm requires compromising between acquisition frequency resolution improvement and reduction in acquisition time. A high-resolution fine acquisition method is proposed to acquire the carrier frequency accurately after the completion of the coarse acquisition of the GPS signals. The proposed method uses Gram-Schmidt orthogonalization to provide robust spectral estimation of satellite Doppler frequency with less computational time. The performance of proposed method is evaluated against of the computational load for GPS L1 signal. Its performance was compared to the state of the art FFT and zero-padding FFT-based fine acquisition algorithms. The experi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584446]").text(description); $(".js-view-count[data-work-id=16584446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584446']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584446,"title":"High Resolution Fine Acquisition Algorithm for GNSS Receivers","internal_url":"https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584445"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584445/A_Smart_Reconfigurable_Multi_sensor_Multi_filter_Navigation_Engine_with_Modular_Architecture_Design_for_Plug_and_Play_Navigation"><img alt="Research paper thumbnail of A Smart Reconfigurable Multi-sensor Multi-filter Navigation Engine with Modular Architecture Design for Plug-and-Play Navigation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584445/A_Smart_Reconfigurable_Multi_sensor_Multi_filter_Navigation_Engine_with_Modular_Architecture_Design_for_Plug_and_Play_Navigation">A Smart Reconfigurable Multi-sensor Multi-filter Navigation Engine with Modular Architecture Design for Plug-and-Play Navigation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Modern navigation and positioning systems integrate a wide spectrum of sensors and work under var...</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">Modern navigation and positioning systems integrate a wide spectrum of sensors and work under variety of different conditions and contexts. This requires a great level of flexibility in systems design and architecture such that any change in requirements can be handled with minimum time and efforts. At the same time, modern navigation and positioning systems require high degree of smartness and context awareness such that the navigation systems automatically reconfigure themselves to adapt to the current environment. In this context, this paper describes the architecture and framework of an ongoing development of a smart reconfigurable multi-sensor integrated navigation and positioning system. The proposed design and framework are based on object oriented design concepts and address key challenges in multi-sensors navigation systems, such as, hardware abstraction, sensors data abstraction, filter encapsulation, multi-filtering, multi-threading, synchronization, interoperability with...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584445"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584445"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584445; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584445]").text(description); $(".js-view-count[data-work-id=16584445]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584445; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584445']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16584444"><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/16584444/Adaptive_Integrated_Indoor_Pedestrian_Tracking_System_Using_MEMS_sensors_and_Hybrid_WiFi_Bluetooth_Beacons_With_Optimized_Grid_based_Bayesian_Filtering_Algorithm"><img alt="Research paper thumbnail of Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid-based Bayesian Filtering Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/40036594/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/16584444/Adaptive_Integrated_Indoor_Pedestrian_Tracking_System_Using_MEMS_sensors_and_Hybrid_WiFi_Bluetooth_Beacons_With_Optimized_Grid_based_Bayesian_Filtering_Algorithm">Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid-based Bayesian Filtering Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positi...</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">With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positioning, tracking, and navigation is becoming achievable. Among many platforms that need to be localized and tracked are pedestrians. A reliable indoor pedestrians tracking has a wide range of applications such as healthcare, retail, rescue missions and context-awareness applications. This paper introduces a calibration-free hybrid indoor positioning system that utilizes inertial sensors (INS), wireless local area networks (WLAN), and low-cost Blue-tooth low-energy (BLE) wireless beacons. BLE beacons are becoming very popular in retails and they can be easily installed in any indoor environment. To deal with jumpy and noisy nature of wireless positioning indoors, and to cancel out the unbounded drifts associated with INS, this work investigates the utilization of Grid-based nonlinear Bayesian filtering to fuse all the aforementioned sensors measurements. The motion-updated prior is modeled...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a1e16cf95817f0c2698507efc3537e3a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036594,"asset_id":16584444,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036594/download_file?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="16584444"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584444"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584444; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584444]").text(description); $(".js-view-count[data-work-id=16584444]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584444; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584444']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="16584443"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU"><img alt="Research paper thumbnail of Small Pipeline Trajectory Estimation Using MEMS based IMU" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU">Small Pipeline Trajectory Estimation Using MEMS based IMU</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Advances in Micro-Electro-Mechanical-Systems (MEMS) technology combined with the miniaturization ...</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">Advances in Micro-Electro-Mechanical-Systems (MEMS) technology combined with the miniaturization of electronics, have made it possible to produce low cost and lightweight chip-based inertial sensors. These chips are small, lightweight, consumes very little power, and reliable. It has therefore found a wide spectrum of applications in the automotive and other industrial applications. MEMS technology, therefore, can be used to develop navigation systems that are inexpensive, small, and consume low power (microwatt). However, the current achieved performance by these low cost sensors is relatively poor due to their sensor errors. Nowadays, high-end tactical grade Inertial Measurement Units (IMUs) are used in pigging applications for estimating the trajectory of geo-pigs in pipelines. These types of IMUs are accurate enough to provide an acceptable solution. However, the size of the tactical grade IMUs is large and cannot be used in the small diameter pipelines (8” or less). This paper ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584443"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584443"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584443; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584443]").text(description); $(".js-view-count[data-work-id=16584443]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584443; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584443']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584443]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584443,"title":"Small Pipeline Trajectory Estimation Using MEMS based IMU","internal_url":"https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584442"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters"><img alt="Research paper thumbnail of Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters">Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces a novel methodology to improve indoor tracking systems in local area wirele...</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 introduces a novel methodology to improve indoor tracking systems in local area wireless networks (WLAN) based on received signal strength (RSS). The proposed methodology addresses significant shortcomings and drawbacks in current existing indoor WLAN-based tracking systems. First, it does not need offline calibration or manual data collection. Instead, it uses an automatic crowdsourcing-based technique with a Fast Orthogonal Search algorithm to process sparse unequally-spaced RSS measurements. Second, it solves the hardware variations problem where multiple mobile devices from different manufacturers with different RSS levels are to be tracked. Third, the proposed system handles both short-term and long-term RSS variations through a novel multiple-particle filter approach. Finally, tracking is performed through a tightly-coupled particle filter algorithm that fuses RSS observations with a simple pedestrian walking model. Experimental and simulation work showed that the p...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584442"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584442"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584442; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584442]").text(description); $(".js-view-count[data-work-id=16584442]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584442; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584442']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584442]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584442,"title":"Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters","internal_url":"https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584441"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources"><img alt="Research paper thumbnail of Robust fine acquisition algorithm for GPS receiver with limited resources" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources">Robust fine acquisition algorithm for GPS receiver with limited resources</a></div><div class="wp-workCard_item"><span>GPS Solutions</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The signal acquisition stage of a GPS receiver detects GPS satellites in view and provid...</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">ABSTRACT The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse estimate of the GPS signal Doppler frequency shift and code delay for use by the tracking loops. The accuracy of the signal acquisition has a direct influence on the tracking performance. The implementation of a GPS signal acquisition algorithm requires compromising between acquisition frequency resolution improvement and reduction in acquisition time. A robust fine acquisition method is proposed to acquire the carrier frequency accurately after the completion of the coarse acquisition of the GPS signals. The proposed method uses Gram-Schmidt orthogonalization to provide robust spectral estimation of satellite Doppler frequency with less computational time. The proposed method starts after the coarse acquisition has been accomplished. The C/A code phase is striped off from the carrier signal. Then, sinusoidal candidate functions are generated at each of the frequencies range of interest, which is typically set around the estimated Doppler shift acquired from the coarse acquisition stage. Finally, an orthogonal search algorithm is utilized to detect the carrier frequency accurately. The performance of the proposed method is evaluated against of the computational load and the effects of the noise. Its performance was also compared to the state-of-the-art FFT and zero-padding FFT-based fine acquisition algorithms. The simulation and experimental results show that the proposed method outperforms existing methods and has sufficient acquisition accuracy for its application in the real world.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584441"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584441"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584441; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584441]").text(description); $(".js-view-count[data-work-id=16584441]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584441; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584441']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584441]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584441,"title":"Robust fine acquisition algorithm for GPS receiver with limited resources","internal_url":"https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584440"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584440/Testing_the_applicability_of_fiber_optic_gyroscopes_for_azimuth_monitoring_for_measurement_while_drilling_processes_in_the_oil_industry"><img alt="Research paper thumbnail of Testing the applicability of fiber optic gyroscopes for azimuth monitoring for measurement-while-drilling processes in the oil industry" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584440/Testing_the_applicability_of_fiber_optic_gyroscopes_for_azimuth_monitoring_for_measurement_while_drilling_processes_in_the_oil_industry">Testing the applicability of fiber optic gyroscopes for azimuth monitoring for measurement-while-drilling processes in the oil industry</a></div><div class="wp-workCard_item"><span>IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062)</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Aboelmagd Noureldin&amp;amp;amp;#x27;, Herb Table?, Dave Irvine-Halliday&amp;amp;amp;#x27; an...</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">... Aboelmagd Noureldin&amp;amp;amp;#x27;, Herb Table?, Dave Irvine-Halliday&amp;amp;amp;#x27; and Martin Mintchev&amp;amp;amp;#x27; 1 Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada and 21nternational Downhole Equipment, Ltd., Edmonton, Alberta, Canada ABSTRACT ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584440"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584440"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584440; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584440]").text(description); $(".js-view-count[data-work-id=16584440]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584440; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584440']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584440]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584440,"title":"Testing the applicability of fiber optic gyroscopes for azimuth monitoring for measurement-while-drilling processes in the oil industry","internal_url":"https://www.academia.edu/16584440/Testing_the_applicability_of_fiber_optic_gyroscopes_for_azimuth_monitoring_for_measurement_while_drilling_processes_in_the_oil_industry","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584439"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584439/Computer_modelling_of_microelectronic_closed_loop_fiber_optic_gyroscope"><img alt="Research paper thumbnail of Computer modelling of microelectronic closed loop fiber optic gyroscope" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584439/Computer_modelling_of_microelectronic_closed_loop_fiber_optic_gyroscope">Computer modelling of microelectronic closed loop fiber optic gyroscope</a></div><div class="wp-workCard_item"><span>Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411)</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Page 1. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering S...</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">Page 1. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering Shaw Conference Center, Edmonton, Alberta, Canada May 9-12 1999 Computer Modelling Of Microelectronic Closed Loop Fiber Optic Gyroscope ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584439"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584439"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584439; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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The device can be strapped or non-strapped to the platform, and where non-strapped, the mobility of the device may be constrained or unconstrained within the platform and the device may be moved or tilted to any orientation within the platform, without degradation in performance of determining the mode of motion. This method can utilize measurements (readings) from sensors in the device (such as for example, accelerometers, gyroscopes, etc.) whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning). The present method and system may be used in any one or both of two different phases, a model building phase or a model utilization phase.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0c56086d3caff7f4a96a88461f292430" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":37867266,"asset_id":12282810,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/37867266/download_file?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="12282810"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="12282810"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12282810; 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$(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="7752302"><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/7752302/Using_Portable_Device_Sensors_to_Recognize_Height_Changing_Modes_of_Motion"><img alt="Research paper thumbnail of Using Portable Device Sensors to Recognize Height Changing Modes of Motion" class="work-thumbnail" src="https://attachments.academia-assets.com/34269344/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/7752302/Using_Portable_Device_Sensors_to_Recognize_Height_Changing_Modes_of_Motion">Using Portable Device Sensors to Recognize Height Changing Modes of Motion</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/AboelmagdNoureldin">Aboelmagd Noureldin</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://shams.academia.edu/MostafaElhoushi">Mostafa Elhoushi</a></span></div><div class="wp-workCard_item"><span>Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International</span><span>, May 13, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In portable navigation, the need to recognize the motion mode of the user, is useful - if not nec...</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 portable navigation, the need to recognize the motion mode of the user, is useful - if not necessary - to improve the positioning estimation. This paper explains the use of sensors in a portable device, such as a smartphone, to recognize the user's mode of motion when a change in height is detected. The modes of motion detected are walking up or down stairs, taking the elevator, and standing or walking on an escalator. The portable device contains an accelerometer triad, gyroscope triad, a barometer, and occasionally a magnetometer triad. The solution is dependent on the sensors, and does not require satellite or wireless positioning. The height motion mode recognition module has been implemented in real-time on several brands of various consumer portable devices, including smartphones, tablets, smartwatches, and smart glasses.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34f9d593abc6345e94966507713dffe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34269344,"asset_id":7752302,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34269344/download_file?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="7752302"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7752302"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7752302; 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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="7673806"><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/7673806/Robust_Motion_Mode_Recognition_for_Portable_Navigation_Independent_on_Device_Usage"><img alt="Research paper thumbnail of Robust Motion Mode Recognition for Portable Navigation Independent on Device Usage" class="work-thumbnail" src="https://attachments.academia-assets.com/34207749/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/7673806/Robust_Motion_Mode_Recognition_for_Portable_Navigation_Independent_on_Device_Usage">Robust Motion Mode Recognition for Portable Navigation Independent on Device Usage</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://shams.academia.edu/MostafaElhoushi">Mostafa Elhoushi</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/AboelmagdNoureldin">Aboelmagd Noureldin</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Portable navigation has become increasingly prevalent in daily activities. The need for accurate ...</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">Portable navigation has become increasingly prevalent in daily activities. The need for accurate user positioning information, including a person's location and velocity, when using a portable device (such as a cell phone, tablet, or even a smart watch) is growing in various fields. Knowing the user's mode of motion or conveyance allows appropriate algorithms or constraints, related to each mode, to be used to estimate a more accurate position and velocity. The modes covered in this paper are walking, running, cycling, and land-based vessels (including vehicles, truck, buses, and trains which include light rail trains and subways). The work discussed in this paper involves the use of sensors — with and without Global Navigation Satellite Systems (GNSS) signal availability —in portable devices to help recognize the mode of motion for an arbitrary user, an arbitrary use case — whether the device is held in the hand, in the pocket, or at the ear, etc. — and an arbitrary orientation of the device.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="89295804bacab71081869d25635778bc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34207749,"asset_id":7673806,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34207749/download_file?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="7673806"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7673806"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7673806; 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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="3702262" id="papers"><div class="js-work-strip profile--work_container" data-work-id="111447446"><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/111447446/Low_Cost_Real_Time_PPP_INS_Integration_for_Automated_Land_Vehicles"><img alt="Research paper thumbnail of Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles" class="work-thumbnail" src="https://attachments.academia-assets.com/108985138/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/111447446/Low_Cost_Real_Time_PPP_INS_Integration_for_Automated_Land_Vehicles">Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The last decade has witnessed a growing demand for precise positioning in many applications inclu...</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">The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the develop...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ce64f046ac96e2e6e710c73d16da43fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108985138,"asset_id":111447446,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108985138/download_file?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="111447446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111447446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111447446; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16584485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584485/IADC_SPE_128968"><img alt="Research paper thumbnail of IADC/SPE 128968" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/16584485/IADC_SPE_128968">IADC/SPE 128968</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="16584485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584485; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584485]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584485,"title":"IADC/SPE 128968","internal_url":"https://www.academia.edu/16584485/IADC_SPE_128968","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584457"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584457/An_Enhanced_Error_Model_for_EKF_Based_Tightly_Coupled_Integration_of_GPS_and_Land_Vehicles_Motion_Sensors"><img alt="Research paper thumbnail of An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584457/An_Enhanced_Error_Model_for_EKF_Based_Tightly_Coupled_Integration_of_GPS_and_Land_Vehicles_Motion_Sensors">An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors</a></div><div class="wp-workCard_item"><span>Sensors (Basel, Switzerland)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, lo...</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">Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers&#39; measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer&#39;s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors....</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584457"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584457"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584457; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584457]").text(description); $(".js-view-count[data-work-id=16584457]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584457; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584457']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584457]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584457,"title":"An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors","internal_url":"https://www.academia.edu/16584457/An_Enhanced_Error_Model_for_EKF_Based_Tightly_Coupled_Integration_of_GPS_and_Land_Vehicles_Motion_Sensors","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584454"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system"><img alt="Research paper thumbnail of Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system">Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Author: R. Sharaf, M. Tarbouchi, A. El-Shafie and A. Noureldin. Meeting: Proceedings of the 2...</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">... Author: R. Sharaf, M. Tarbouchi, A. El-Shafie and A. Noureldin. Meeting: Proceedings of the 2005 National Technical Meeting of The Institute of Navigation January 24 - 26, 2005 The Catamaran Resort Hotel San Diego, CA. Page(s): 235 - 242. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584454"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584454"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584454; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584454]").text(description); $(".js-view-count[data-work-id=16584454]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584454; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584454']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584454]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584454,"title":"Real-time implementation of INS/GPS data fusion utilizing adaptive neuro-fuzzy inference system","internal_url":"https://www.academia.edu/16584454/Real_time_implementation_of_INS_GPS_data_fusion_utilizing_adaptive_neuro_fuzzy_inference_system","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584451"><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/16584451/Dynamic_Online_Calibrated_Radio_Maps_for_Indoor_Positioning_in_Wireless_Local_Area_Networks"><img alt="Research paper thumbnail of Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/40036599/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/16584451/Dynamic_Online_Calibrated_Radio_Maps_for_Indoor_Positioning_in_Wireless_Local_Area_Networks">Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Context-awareness and Location-Based-Services are of great importance in mobile computing environ...</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">Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c6b7a734215a96e9ec9bf673c2f06180" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036599,"asset_id":16584451,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036599/download_file?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="16584451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584451; 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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="16584450"><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/16584450/Real_time_Cycle_slip_Detection_and_Correction_for_Land_Vehicle_Navigation_Using_Inertial_Aiding"><img alt="Research paper thumbnail of Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation Using Inertial Aiding" class="work-thumbnail" src="https://attachments.academia-assets.com/40036598/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/16584450/Real_time_Cycle_slip_Detection_and_Correction_for_Land_Vehicle_Navigation_Using_Inertial_Aiding">Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation Using Inertial Aiding</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Carrier phase measurements require resolution of integer ambiguities before precise positioning c...</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">Carrier phase measurements require resolution of integer ambiguities before precise positioning can be achieved. The GPS receiver can keep track of the integer number of cycles as long as the receiver maintains lock to the satellite signal. However, in reality, the GPS signal could be interrupted momentary by some disturbing factors leading to a discontinuity of an integer number of cycles in the measured carrier phase. This interruption in the counting of cycles in the carrier phase measurements is known as a cycle slip. After a cycle slip, ambiguities need to be resolved again or cycle slips need to be corrected to resume the precise positioning/navigation process. These cycle slips can, to some extent, be detected and fixed to avoid delay and computation complexity attributed to the process of integer ambiguity resolution. Capitalizing on the complementary characteristics of INS and GPS, INS is used to provide external information to detect and correct cycle slips. Lately, MEMS grade inertial sensors are being used for low cost navigation applications. Moreover, recent research is geared towards the use of fewer numbers of sensors avoiding their complex errors and reducing the cost. This paper introduces integration of GPS and reduced inertial sensor system (RISS) to address the problem of cycle slips. The performance of proposed method is examined on several real-life land vehicle trajectories which included various challenging driving scenarios including high and slow speeds, sudden accelerations and decelerations, sharp turns and steep slopes etc. Results demonstrate the effectiveness of the proposed algorithm in these severe conditions which cause intensive and variable-sized cycle slips. This research has a direct influence on navigation in challenging environments where frequent cycle slips occur and resolving integer ambiguities is not affordable because of time and computational constraints. An additional consequence of this research is the significant reduction in the cost of an integrated system due to the use of fewer MEMS inertial sensors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03e92c91ae1ae5691a245e3fc6834e86" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036598,"asset_id":16584450,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036598/download_file?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="16584450"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584450"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584450; 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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="16584448"><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/16584448/An_Adaptive_Positioning_System_for_Smartphones_in_Zigbee_Networks_Using_Channel_Decomposition_and_Particle_Swarm_Optimization"><img alt="Research paper thumbnail of An Adaptive Positioning System for Smartphones in Zigbee Networks Using Channel Decomposition and Particle Swarm Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/40036575/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/16584448/An_Adaptive_Positioning_System_for_Smartphones_in_Zigbee_Networks_Using_Channel_Decomposition_and_Particle_Swarm_Optimization">An Adaptive Positioning System for Smartphones in Zigbee Networks Using Channel Decomposition and Particle Swarm Optimization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ZigBee wireless sensor networks has the benefit of superior optimization in power consumption and...</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">ZigBee wireless sensor networks has the benefit of superior optimization in power consumption and extremely long battery life. In near future, the handheld devices are expected to incorporate support for wireless sensor networks technology, and these devices can become active elements in ZigBee-based applications such as automated buildings and advanced metering systems. Use of these applications is also promising for the expansion of location services into environments that do not have access to Global Navigation Satellite Systems (GNSS). Compared with the ToA (Time of Arrival) and TDoA (Time difference of Arrival) methods, RSSI (Received Signal Strength Indicator) has several advantages. It does not require additional hardware. Owing to the simplicity of RSSI, the most recent studies on localization using wireless sensor networks use RSSI-based algorithms. Previous works RSSI-based location estimation methods discussed by the literature depend on the impractical assumption that si...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b090cb0332d60617432aaf1ca6b51394" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036575,"asset_id":16584448,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036575/download_file?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="16584448"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584448"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584448; 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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="16584447"><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/16584447/Enhancing_Vision_Aided_GNSS_INS_Navigation_Systems_Using_Nonlinear_Modeling_Techniques_Based_on_Fast_Orthogonal_Search_with_Double_Filtering_Mechanism"><img alt="Research paper thumbnail of Enhancing Vision-Aided GNSS/INS Navigation Systems Using Nonlinear Modeling Techniques Based on Fast Orthogonal Search with Double-Filtering Mechanism" class="work-thumbnail" src="https://attachments.academia-assets.com/40036597/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/16584447/Enhancing_Vision_Aided_GNSS_INS_Navigation_Systems_Using_Nonlinear_Modeling_Techniques_Based_on_Fast_Orthogonal_Search_with_Double_Filtering_Mechanism">Enhancing Vision-Aided GNSS/INS Navigation Systems Using Nonlinear Modeling Techniques Based on Fast Orthogonal Search with Double-Filtering Mechanism</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In vision-aided INS/GNSS integrated navigation systems, several errors contribute to significant ...</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 vision-aided INS/GNSS integrated navigation systems, several errors contribute to significant drifts in the absence of GNSS and during severe GNSS interruptions. These errors includes cameras distortion, camera calibration, camera misalignments, and feature extraction/tracking errors. In addition, inertial sensors biases, drifts, non-orthogonality, non-normality and misalignment also significantly affect the dead-reckoning navigation accuracy in the absence of GNSS. Due to the complex nonlinearity inherent in these errors, this paper introduces a double-filter mechanism enhanced by nonlinear error modeling for multi-sensor vision-aided integrated navigation systems for land vehicles. The proposed system integrates GNSS, vision, INS, and odometry. The proposed methodology tries to reduce the effects of nonlinear errors that propagate through different components of the integrated navigation system. The proposed methodology can work in real-time due to the utilization of a fast ort...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad46a90ebddbd5872c186200182835a6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036597,"asset_id":16584447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036597/download_file?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="16584447"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584447]").text(description); $(".js-view-count[data-work-id=16584447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584447']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="16584446"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers"><img alt="Research paper thumbnail of High Resolution Fine Acquisition Algorithm for GNSS Receivers" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers">High Resolution Fine Acquisition Algorithm for GNSS Receivers</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse...</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">The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse estimate of the GPS signal Doppler frequency shift and code delay for use by the tracking loops. The accuracy of the signal acquisition has a direct influence on the tracking performance. The implementation of a GPS signal acquisition algorithm requires compromising between acquisition frequency resolution improvement and reduction in acquisition time. A high-resolution fine acquisition method is proposed to acquire the carrier frequency accurately after the completion of the coarse acquisition of the GPS signals. The proposed method uses Gram-Schmidt orthogonalization to provide robust spectral estimation of satellite Doppler frequency with less computational time. The performance of proposed method is evaluated against of the computational load for GPS L1 signal. Its performance was compared to the state of the art FFT and zero-padding FFT-based fine acquisition algorithms. The experi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584446"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584446"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584446; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584446]").text(description); $(".js-view-count[data-work-id=16584446]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584446; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584446']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584446]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584446,"title":"High Resolution Fine Acquisition Algorithm for GNSS Receivers","internal_url":"https://www.academia.edu/16584446/High_Resolution_Fine_Acquisition_Algorithm_for_GNSS_Receivers","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584445"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584445/A_Smart_Reconfigurable_Multi_sensor_Multi_filter_Navigation_Engine_with_Modular_Architecture_Design_for_Plug_and_Play_Navigation"><img alt="Research paper thumbnail of A Smart Reconfigurable Multi-sensor Multi-filter Navigation Engine with Modular Architecture Design for Plug-and-Play Navigation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584445/A_Smart_Reconfigurable_Multi_sensor_Multi_filter_Navigation_Engine_with_Modular_Architecture_Design_for_Plug_and_Play_Navigation">A Smart Reconfigurable Multi-sensor Multi-filter Navigation Engine with Modular Architecture Design for Plug-and-Play Navigation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Modern navigation and positioning systems integrate a wide spectrum of sensors and work under var...</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">Modern navigation and positioning systems integrate a wide spectrum of sensors and work under variety of different conditions and contexts. This requires a great level of flexibility in systems design and architecture such that any change in requirements can be handled with minimum time and efforts. At the same time, modern navigation and positioning systems require high degree of smartness and context awareness such that the navigation systems automatically reconfigure themselves to adapt to the current environment. In this context, this paper describes the architecture and framework of an ongoing development of a smart reconfigurable multi-sensor integrated navigation and positioning system. The proposed design and framework are based on object oriented design concepts and address key challenges in multi-sensors navigation systems, such as, hardware abstraction, sensors data abstraction, filter encapsulation, multi-filtering, multi-threading, synchronization, interoperability with...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584445"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584445"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584445; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584445]").text(description); $(".js-view-count[data-work-id=16584445]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584445; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584445']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16584444"><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/16584444/Adaptive_Integrated_Indoor_Pedestrian_Tracking_System_Using_MEMS_sensors_and_Hybrid_WiFi_Bluetooth_Beacons_With_Optimized_Grid_based_Bayesian_Filtering_Algorithm"><img alt="Research paper thumbnail of Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid-based Bayesian Filtering Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/40036594/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/16584444/Adaptive_Integrated_Indoor_Pedestrian_Tracking_System_Using_MEMS_sensors_and_Hybrid_WiFi_Bluetooth_Beacons_With_Optimized_Grid_based_Bayesian_Filtering_Algorithm">Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid-based Bayesian Filtering Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positi...</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">With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positioning, tracking, and navigation is becoming achievable. Among many platforms that need to be localized and tracked are pedestrians. A reliable indoor pedestrians tracking has a wide range of applications such as healthcare, retail, rescue missions and context-awareness applications. This paper introduces a calibration-free hybrid indoor positioning system that utilizes inertial sensors (INS), wireless local area networks (WLAN), and low-cost Blue-tooth low-energy (BLE) wireless beacons. BLE beacons are becoming very popular in retails and they can be easily installed in any indoor environment. To deal with jumpy and noisy nature of wireless positioning indoors, and to cancel out the unbounded drifts associated with INS, this work investigates the utilization of Grid-based nonlinear Bayesian filtering to fuse all the aforementioned sensors measurements. The motion-updated prior is modeled...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a1e16cf95817f0c2698507efc3537e3a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":40036594,"asset_id":16584444,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/40036594/download_file?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="16584444"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584444"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584444; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584444]").text(description); $(".js-view-count[data-work-id=16584444]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584444; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584444']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="16584443"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU"><img alt="Research paper thumbnail of Small Pipeline Trajectory Estimation Using MEMS based IMU" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU">Small Pipeline Trajectory Estimation Using MEMS based IMU</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Advances in Micro-Electro-Mechanical-Systems (MEMS) technology combined with the miniaturization ...</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">Advances in Micro-Electro-Mechanical-Systems (MEMS) technology combined with the miniaturization of electronics, have made it possible to produce low cost and lightweight chip-based inertial sensors. These chips are small, lightweight, consumes very little power, and reliable. It has therefore found a wide spectrum of applications in the automotive and other industrial applications. MEMS technology, therefore, can be used to develop navigation systems that are inexpensive, small, and consume low power (microwatt). However, the current achieved performance by these low cost sensors is relatively poor due to their sensor errors. Nowadays, high-end tactical grade Inertial Measurement Units (IMUs) are used in pigging applications for estimating the trajectory of geo-pigs in pipelines. These types of IMUs are accurate enough to provide an acceptable solution. However, the size of the tactical grade IMUs is large and cannot be used in the small diameter pipelines (8” or less). This paper ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584443"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584443"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584443; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584443]").text(description); $(".js-view-count[data-work-id=16584443]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584443; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584443']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584443]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584443,"title":"Small Pipeline Trajectory Estimation Using MEMS based IMU","internal_url":"https://www.academia.edu/16584443/Small_Pipeline_Trajectory_Estimation_Using_MEMS_based_IMU","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584442"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters"><img alt="Research paper thumbnail of Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters">Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces a novel methodology to improve indoor tracking systems in local area wirele...</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 introduces a novel methodology to improve indoor tracking systems in local area wireless networks (WLAN) based on received signal strength (RSS). The proposed methodology addresses significant shortcomings and drawbacks in current existing indoor WLAN-based tracking systems. First, it does not need offline calibration or manual data collection. Instead, it uses an automatic crowdsourcing-based technique with a Fast Orthogonal Search algorithm to process sparse unequally-spaced RSS measurements. Second, it solves the hardware variations problem where multiple mobile devices from different manufacturers with different RSS levels are to be tracked. Third, the proposed system handles both short-term and long-term RSS variations through a novel multiple-particle filter approach. Finally, tracking is performed through a tightly-coupled particle filter algorithm that fuses RSS observations with a simple pedestrian walking model. Experimental and simulation work showed that the p...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584442"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584442"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584442; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584442]").text(description); $(".js-view-count[data-work-id=16584442]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584442; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584442']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584442]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584442,"title":"Hardware-Independent Automatic Crowdsourcing-Based Hybrid WLAN-RFID Adaptive Indoor Tracking System Using Fast Orthogonal Search and Multiple Particle Filters","internal_url":"https://www.academia.edu/16584442/Hardware_Independent_Automatic_Crowdsourcing_Based_Hybrid_WLAN_RFID_Adaptive_Indoor_Tracking_System_Using_Fast_Orthogonal_Search_and_Multiple_Particle_Filters","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584441"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources"><img alt="Research paper thumbnail of Robust fine acquisition algorithm for GPS receiver with limited resources" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources">Robust fine acquisition algorithm for GPS receiver with limited resources</a></div><div class="wp-workCard_item"><span>GPS Solutions</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The signal acquisition stage of a GPS receiver detects GPS satellites in view and provid...</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">ABSTRACT The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse estimate of the GPS signal Doppler frequency shift and code delay for use by the tracking loops. The accuracy of the signal acquisition has a direct influence on the tracking performance. The implementation of a GPS signal acquisition algorithm requires compromising between acquisition frequency resolution improvement and reduction in acquisition time. A robust fine acquisition method is proposed to acquire the carrier frequency accurately after the completion of the coarse acquisition of the GPS signals. The proposed method uses Gram-Schmidt orthogonalization to provide robust spectral estimation of satellite Doppler frequency with less computational time. The proposed method starts after the coarse acquisition has been accomplished. The C/A code phase is striped off from the carrier signal. Then, sinusoidal candidate functions are generated at each of the frequencies range of interest, which is typically set around the estimated Doppler shift acquired from the coarse acquisition stage. Finally, an orthogonal search algorithm is utilized to detect the carrier frequency accurately. The performance of the proposed method is evaluated against of the computational load and the effects of the noise. Its performance was also compared to the state-of-the-art FFT and zero-padding FFT-based fine acquisition algorithms. The simulation and experimental results show that the proposed method outperforms existing methods and has sufficient acquisition accuracy for its application in the real world.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584441"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584441"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584441; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584441]").text(description); $(".js-view-count[data-work-id=16584441]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16584441; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='16584441']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=16584441]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":16584441,"title":"Robust fine acquisition algorithm for GPS receiver with limited resources","internal_url":"https://www.academia.edu/16584441/Robust_fine_acquisition_algorithm_for_GPS_receiver_with_limited_resources","owner_id":22414092,"coauthors_can_edit":true,"owner":{"id":22414092,"first_name":"Aboelmagd","middle_initials":null,"last_name":"Noureldin","page_name":"AboelmagdNoureldin","domain_name":"independent","created_at":"2014-11-27T23:37:12.043-08:00","display_name":"Aboelmagd Noureldin","url":"https://independent.academia.edu/AboelmagdNoureldin"},"attachments":[]}, 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="16584440"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584440/Testing_the_applicability_of_fiber_optic_gyroscopes_for_azimuth_monitoring_for_measurement_while_drilling_processes_in_the_oil_industry"><img alt="Research paper thumbnail of Testing the applicability of fiber optic gyroscopes for azimuth monitoring for measurement-while-drilling processes in the oil industry" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584440/Testing_the_applicability_of_fiber_optic_gyroscopes_for_azimuth_monitoring_for_measurement_while_drilling_processes_in_the_oil_industry">Testing the applicability of fiber optic gyroscopes for azimuth monitoring for measurement-while-drilling processes in the oil industry</a></div><div class="wp-workCard_item"><span>IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062)</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Aboelmagd Noureldin&amp;amp;amp;#x27;, Herb Table?, Dave Irvine-Halliday&amp;amp;amp;#x27; an...</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">... 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No.99TH8411)</span><span>, 1999</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Page 1. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering S...</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">Page 1. 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$a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="16584436"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/16584436/An_Inertial_Aided_LiDAR_Scan_Matching_Algorithm_for_Multisensor_Land_Based_Navigation"><img alt="Research paper thumbnail of An Inertial-Aided LiDAR Scan Matching Algorithm for Multisensor Land-Based Navigation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/16584436/An_Inertial_Aided_LiDAR_Scan_Matching_Algorithm_for_Multisensor_Land_Based_Navigation">An Inertial-Aided LiDAR Scan Matching Algorithm for Multisensor Land-Based Navigation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes a land-based navigation system that integrates inertial sensors and odometer...</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 describes a land-based navigation system that integrates inertial sensors and odometer with Light Detection and Ranging (LiDAR). In the proposed multisensor navigation system, the popular point-based scan matching algorithm Iterative Closest Point (ICP) is used to estimate the vehicle’s relative translational and rotational changes from raw LiDAR measurements without the need for feature extraction. To accelerate the ICP algorithm and improve the accuracy, the outputs of Inertial Navigation System (INS) and odometry are used as an initial motion guess to enhance the scan matching algorithm by reducing the error in initial alignment. The relative translation and rotation changes from LiDAR are fused with changes from INS/Odometry through Extended Kalman Filter (EKF) in a tightly coupled scheme. Real experiments were conducted to evaluate the performance of the proposed system. Results showed that by integrating LiDAR with INS/Odometry, inertial sensors biases are accuratel...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="16584436"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="16584436"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16584436; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16584436]").text(description); 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