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However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.<\/jats:p>","DOI":"10.1177\/0142331216652467","type":"journal-article","created":{"date-parts":[[2016,6,20]],"date-time":"2016-06-20T22:34:06Z","timestamp":1466462046000},"page":"251-260","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Group sparsity based imaging algorithm for TWRI under wall parameter uncertainties"],"prefix":"10.1177","volume":"40","author":[{"given":"Yanpeng","family":"Sun","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Liaoning, China"},{"name":"College of Electronic Information Engineering, Shenyang Aerospace University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northeastern University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Shenyang Aerospace University, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,6,20]]},"reference":[{"key":"bibr1-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2008.03.003"},{"key":"bibr2-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2345259"},{"key":"bibr3-0142331216652467","first-page":"943","volume":"14","author":"Cai XJ","year":"2008","journal-title":"International Journal of Innovative Computing, Information and Control"},{"key":"bibr4-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2010.2044837"},{"key":"bibr5-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2012.2230314"},{"key":"bibr6-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2016270"},{"key":"bibr7-0142331216652467","first-page":"843","volume":"14","author":"Henning P","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"bibr8-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.22.2.021003"},{"key":"bibr9-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2008.01.006"},{"key":"bibr10-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2312203"},{"key":"bibr11-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2013.120528"},{"key":"bibr12-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638817"},{"key":"bibr13-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/RADAR.2013.6586115"},{"key":"bibr14-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2011.2171946"},{"key":"bibr15-0142331216652467","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2015.2398115"},{"key":"bibr16-0142331216652467","first-page":"256","author":"Mansour H","year":"2013","journal-title":"IEEE Computational Advances Multi-Sensor Adaptive Processing (CAMSAP)"},{"key":"bibr17-0142331216652467","unstructured":"Mccorkle JW (1998) Method and system for forming image by backprojection. 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