Skip to main content
placeholder image

Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery

Conference Paper


Download full-text (Open Access)

Abstract


  • This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to previous approaches where the signal at each antenna location is reconstructed independently. Furthermore, to promote sparsity and improve signal reconstruction accuracy, a sparsifying wavelet dictionary is employed in the sparse signal recovery. Following signal reconstruction, a subspace projection technique is applied to remove wall clutter, prior to image formation. Experimental results on real data show that the proposed approach produces significantly higher reconstruction accuracy and requires far fewer measurements compared to the single-signal model, where each antenna signal is reconstructed independently.

Publication Date


  • 2014

Citation


  • V. Tang, A. Bouzerdoum, S. Phung & F. Tivive , "Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014, pp. 7804-7808.

Scopus Eid


  • 2-s2.0-84905270430

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3598&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2589

Start Page


  • 7804

End Page


  • 7808

Abstract


  • This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to previous approaches where the signal at each antenna location is reconstructed independently. Furthermore, to promote sparsity and improve signal reconstruction accuracy, a sparsifying wavelet dictionary is employed in the sparse signal recovery. Following signal reconstruction, a subspace projection technique is applied to remove wall clutter, prior to image formation. Experimental results on real data show that the proposed approach produces significantly higher reconstruction accuracy and requires far fewer measurements compared to the single-signal model, where each antenna signal is reconstructed independently.

Publication Date


  • 2014

Citation


  • V. Tang, A. Bouzerdoum, S. Phung & F. Tivive , "Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), 2014, pp. 7804-7808.

Scopus Eid


  • 2-s2.0-84905270430

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3598&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2589

Start Page


  • 7804

End Page


  • 7808