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Indoor scene reconstruction for through-the-wall radar imaging using low-rank and sparsity constraints

Conference Paper


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Abstract


  • This paper addresses the problem of indoor scene reconstruction in compressed sensing through-the-wall radar imaging. The proposed method is motivated by two observations that wall reflections reside in a low-rank subspace and the imaged scene tends to be sparse. The task of mitigating the wall reflections and reconstructing an image of the scene behind-the-wall is cast as a joint low-rank and sparsity constrained optimization problem, where a low-rank matrix captures the wall returns and a sparse matrix represents the formed image. An iterative algorithm is developed to estimate the low-rank matrix and the sparse scene vector from a reduced measurement set. Experimental results using real radar data show that the proposed model is very effective at reconstructing the indoor image and removing wall clutter.

Publication Date


  • 2016

Citation


  • V. H. Tang, A. Bouzerdoum, S. L. Phung & F. H. C. Tivive , "Indoor scene reconstruction for through-the-wall radar imaging using low-rank and sparsity constraints," in 2016 IEEE Radar Conference (RadarConf), 2016, pp. 1-4.

Scopus Eid


  • 2-s2.0-84978198726

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6949&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5920

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • United States

Abstract


  • This paper addresses the problem of indoor scene reconstruction in compressed sensing through-the-wall radar imaging. The proposed method is motivated by two observations that wall reflections reside in a low-rank subspace and the imaged scene tends to be sparse. The task of mitigating the wall reflections and reconstructing an image of the scene behind-the-wall is cast as a joint low-rank and sparsity constrained optimization problem, where a low-rank matrix captures the wall returns and a sparse matrix represents the formed image. An iterative algorithm is developed to estimate the low-rank matrix and the sparse scene vector from a reduced measurement set. Experimental results using real radar data show that the proposed model is very effective at reconstructing the indoor image and removing wall clutter.

Publication Date


  • 2016

Citation


  • V. H. Tang, A. Bouzerdoum, S. L. Phung & F. H. C. Tivive , "Indoor scene reconstruction for through-the-wall radar imaging using low-rank and sparsity constraints," in 2016 IEEE Radar Conference (RadarConf), 2016, pp. 1-4.

Scopus Eid


  • 2-s2.0-84978198726

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6949&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5920

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • United States