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Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint Bayesian sparse representation

Conference Paper


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Abstract


  • This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image is reconstructed by a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results show that the proposed approach improves reconstruction accuracy and produces a composite scene image in which the targets are enhanced and the background clutter is attenuated.

Publication Date


  • 2015

Citation


  • V. Tang, A. Bouzerdoum, S. L. Phung & F. Tivive , "Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint Bayesian sparse representation," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 2419-2423.

Scopus Eid


  • 2-s2.0-84946091732

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6645&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5617

Start Page


  • 2419

End Page


  • 2423

Place Of Publication


  • United States

Abstract


  • This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image is reconstructed by a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results show that the proposed approach improves reconstruction accuracy and produces a composite scene image in which the targets are enhanced and the background clutter is attenuated.

Publication Date


  • 2015

Citation


  • V. Tang, A. Bouzerdoum, S. L. Phung & F. Tivive , "Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint Bayesian sparse representation," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 2419-2423.

Scopus Eid


  • 2-s2.0-84946091732

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6645&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5617

Start Page


  • 2419

End Page


  • 2423

Place Of Publication


  • United States