A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity</title> <meta name="description" content="A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity"> <meta name="keywords" content="Complex-valued signal processing, synthetic aperture radar (SAR), 2-D radar imaging, compressive sensing, Sparse Bayesian learning."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity"> <meta name="citation_author" content="Pan Long"> <meta name="citation_author" content="Bi Dongjie"> <meta name="citation_author" content="Li Xifeng"> <meta name="citation_author" content="Xie Yongle"> <meta name="citation_publication_date" content="2019/04/01"> <meta name="citation_journal_title" content="International Journal of Electronics and Communication Engineering"> <meta name="citation_volume" content="13"> <meta name="citation_issue" content="5"> <meta name="citation_firstpage" content="323"> <meta name="citation_lastpage" content="330"> <meta name="citation_pdf_url" content="https://publications.waset.org/10010396/pdf"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value=""> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33093</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pan%20Long">Pan Long</a>, <a href="https://publications.waset.org/search?q=Bi%20Dongjie"> Bi Dongjie</a>, <a href="https://publications.waset.org/search?q=Li%20Xifeng"> Li Xifeng</a>, <a href="https://publications.waset.org/search?q=Xie%20Yongle"> Xie Yongle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals. <iframe src="https://publications.waset.org/10010396.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Complex-valued%20signal%20processing" title="Complex-valued signal processing">Complex-valued signal processing</a>, <a href="https://publications.waset.org/search?q=synthetic%20aperture%0D%0Aradar%20%28SAR%29" title=" synthetic aperture radar (SAR)"> synthetic aperture radar (SAR)</a>, <a href="https://publications.waset.org/search?q=2-D%20radar%20imaging" title=" 2-D radar imaging"> 2-D radar imaging</a>, <a href="https://publications.waset.org/search?q=compressive%20sensing" title=" compressive sensing"> compressive sensing</a>, <a href="https://publications.waset.org/search?q=Sparse%0D%0ABayesian%20learning." title=" Sparse Bayesian learning."> Sparse Bayesian learning.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.3298811" target="_blank">doi.org/10.5281/zenodo.3298811</a> </p> <a href="https://publications.waset.org/10010396/a-generalized-sparse-bayesian-learning-algorithm-for-near-field-synthetic-aperture-radar-imaging-by-exploiting-impropriety-and-noncircularity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010396/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010396/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010396/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010396/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010396/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010396/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010396/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010396/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010396/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010396/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1525</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] T¨ulay Adali, Peter J Schreier, and Louis L Scharf. Complex-valued signal processing: The proper way to deal with impropriety. IEEE Transactions on Signal Processing, 59(11):5101–5125, 2011. <br>[2] Maher Al-Shoukairi, Philip Schniter, and Bhaskar D Rao. A gamp-based low complexity sparse bayesian learning algorithm. IEEE Transactions on Signal Processing, 66(2):294–308, 2018. <br>[3] Claude Berrou and Alain Glavieux. Near optimum error correcting coding and decoding: Turbo-codes. The best of the best: fifty years of communications and networking research, 45, 2007. <br>[4] Dongjie Bi, Yongle Xie, Xifeng Li, and Yahong Rosa Zheng. Efficient 2-d synthetic aperture radar image reconstruction from compressed sampling using a parallel operator splitting structure. Digital Signal Processing, 50:171–179, 2016. <br>[5] Dongjie Bi, Yongle Xie, Lan Ma, Xifeng Li, Xiahan Yang, and Yahong Rosa Zheng. Multifrequency compressed sensing for 2-d near-field synthetic aperture radar image reconstruction. IEEE Transactions on Instrumentation and Measurement, 66(4):777–791, 2017. <br>[6] Emmanuel J Cand`es and Michael B Wakin. An introduction to compressive sampling <br>[a sensing/sampling paradigm that goes against the common knowledge in data acquisition]. IEEE signal processing magazine, 25(2):21–30, 2008. <br>[7] Matteo Carlin, Paolo Rocca, Giacomo Oliveri, Federico Viani, and Andrea Massa. Directions-of-arrival estimation through bayesian compressive sensing strategies. IEEE Transactions on Antennas and Propagation, 61(7):3828–3838, 2013. <br>[8] M¨ujdat C¸ etin and William Clement Karl. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Transactions on Image Processing, 10(4):623–631, 2001. <br>[9] David L Donoho, Arian Maleki, and Andrea Montanari. Message passing algorithms for compressed sensing: I. motivation and construction. In 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo), pages 1–5. IEEE, 2010. <br>[10] Joachim HG Ender. On compressive sensing applied to radar. Signal Processing, 90(5):1402–1414, 2010. <br>[11] Hichem Guerboukha, Kathirvel Nallappan, and Maksim Skorobogatiy. Exploiting k-space/frequency duality toward real-time terahertz imaging. Optica, 5(2):109–116, 2018. <br>[12] Qinghua Guo and Defeng David Huang. A concise representation for the soft-in soft-out lmmse detector. IEEE Communications Letters, 15(5):566–568, 2011. <br>[13] Gabor Hannak, Alessandro Perelli, Norbert Goertz, Gerald Matz, and Mike E Davies. Performance analysis of approximate message passing for distributed compressed sensing. IEEE Journal of Selected Topics in Signal Processing, 12(5):857–870, 2018. <br>[14] Mario Huemer, Oliver Lang, and Christian Hofbauer. Component-wise conditionally unbiased widely linear mmse estimation. Signal Processing, 133:227–239, 2017. <br>[15] Sergey Kharkovsky and Reza Zoughi. Microwave and millimeter wave nondestructive testing and evaluation-overview and recent advances. IEEE Instrumentation & Measurement Magazine, 10(2):26–38, 2007. <br>[16] Arian Maleki, Laura Anitori, Zai Yang, and Richard G Baraniuk. Asymptotic analysis of complex lasso via complex approximate message passing (camp). IEEE Transactions on Information Theory, 59(7):4290–4308, 2013. <br>[17] Xiangming Meng, Sheng Wu, Linling Kuang, and Jianhua Lu. Concise derivation of complex bayesian approximate message passing via expectation propagation. arXiv preprint arXiv:1509.08658, 2015. <br>[18] Xiangming Meng, Sheng Wu, and Jiang Zhu. A unified bayesian inference framework for generalized linear models. IEEE Signal Processing Letters, 25(3):398–402, 2018. <br>[19] Xiangming Meng and Jiang Zhu. A generalized sparse bayesian learning algorithm for 1-bit doa estimation. IEEE Communications Letters, 22(7):1414–1417, 2018. <br>[20] Meenu Rani, SB Dhok, and RB Deshmukh. A systematic review of compressive sensing: Concepts, implementations and applications. IEEE Access, 6:4875–4894, 2018. <br>[21] Peter J Schreier and Louis L Scharf. Statistical signal processing of complex-valued data: the theory of improper and noncircular signals. Cambridge university press, 2010. <br>[22] David M Sheen, Douglas L McMakin, and Thomas E Hall. Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transactions on microwave theory and techniques, 49(9):1581–1592, 2001. <br>[23] Qisong Wu, Yimin D Zhang, Moeness G Amin, and Braham Himed. Complex multitask bayesian compressive sensing. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3375–3379. IEEE, 2014. <br>[24] Zhimin Xu, Wai Lam Chan, Daniel M Mittleman, and Edmund Y Lam. Sparse reconstruction of complex signals in compressed sensing terahertz imaging. In Signal Recovery and Synthesis, page STuA4. Optical Society of America, 2009. <br>[25] Muhammet Emin Yanik and Murat Torlak. Near-field mimo-sar millimeter-wave imaging with sparsely sampled aperture data. IEEE Access, 7:31801–31819, 2019. <br>[26] Siwei Yu, A Shaharyar Khwaja, and Jianwei Ma. Compressed sensing of complex-valued data. Signal Processing, 92(2):357–362, 2012. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

CINXE.COM

A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity