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Enhanced through-the-wall radar imaging using bayesian compressive sensing

Conference Paper


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Abstract


  • In this paper, a distributed compressive sensing (CS) model is proposed to recover missing data samples along the temporal frequency domain for through-the-wall radar imaging (TWRI). Existing CS-based approaches recover the signal from each antenna independently, without considering the correlations among measurements. The proposed approach, on the other hand, exploits the structure or correlation in the signals received across the array aperture by using a hierarchical Bayesian model to learn a shared prior for the joint reconstruction of the high-resolution radar profiles. A backprojection method is then applied to form the radar image. Experimental results on real TWRI data show that the proposed approach produces better radar images using fewer measurements compared to existing CS-based TWRI methods. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Publication Date


  • 2013

Citation


  • V. H. Tang, A. Bouzerdoum, S. L. Phung & F. Tivive, "Enhanced through-the-wall radar imaging using bayesian compressive sensing," in Proceedings of SPIE - Compressive Sensing II, 2013, pp. 87170I-1-87170I-12.

Scopus Eid


  • 2-s2.0-84881066047

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2600&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1591

Has Global Citation Frequency


Start Page


  • 87170I-1

End Page


  • 87170I-12

Place Of Publication


  • United States

Abstract


  • In this paper, a distributed compressive sensing (CS) model is proposed to recover missing data samples along the temporal frequency domain for through-the-wall radar imaging (TWRI). Existing CS-based approaches recover the signal from each antenna independently, without considering the correlations among measurements. The proposed approach, on the other hand, exploits the structure or correlation in the signals received across the array aperture by using a hierarchical Bayesian model to learn a shared prior for the joint reconstruction of the high-resolution radar profiles. A backprojection method is then applied to form the radar image. Experimental results on real TWRI data show that the proposed approach produces better radar images using fewer measurements compared to existing CS-based TWRI methods. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Publication Date


  • 2013

Citation


  • V. H. Tang, A. Bouzerdoum, S. L. Phung & F. Tivive, "Enhanced through-the-wall radar imaging using bayesian compressive sensing," in Proceedings of SPIE - Compressive Sensing II, 2013, pp. 87170I-1-87170I-12.

Scopus Eid


  • 2-s2.0-84881066047

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2600&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1591

Has Global Citation Frequency


Start Page


  • 87170I-1

End Page


  • 87170I-12

Place Of Publication


  • United States