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A variational Bayesian approach for multichannel through-wall radar imaging with low-rank and sparse priors

Conference Paper


Abstract


  • © 2020 IEEE This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the observed radar signals, a joint distribution of the observed data and latent variables is formulated by incorporating two important beliefs: low-dimensional structure of wall reflections and joint sparsity among channel images. These priors are modeled through probabilistic distributions whose hyperparameters are treated with a full Bayesian formulation. Furthermore, the paper presents a variational Bayesian inference algorithm that captures wall clutter and provides channel images as full posterior distributions. Experimental results on real data show that the proposed model is very effective at removing wall clutter and enhancing target localization.

UOW Authors


  •   Tang, Van H. (external author)
  •   Bouzerdoum, Abdesselam (external author)
  •   Phung, Son Lam

Publication Date


  • 2020

Citation


  • V. Tang, A. Bouzerdoum & S. Phung, "A variational Bayesian approach for multichannel through-wall radar imaging with low-rank and sparse priors," in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020, pp. 2523-2527.

Scopus Eid


  • 2-s2.0-85091156108

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4379

Start Page


  • 2523

End Page


  • 2527

Abstract


  • © 2020 IEEE This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the observed radar signals, a joint distribution of the observed data and latent variables is formulated by incorporating two important beliefs: low-dimensional structure of wall reflections and joint sparsity among channel images. These priors are modeled through probabilistic distributions whose hyperparameters are treated with a full Bayesian formulation. Furthermore, the paper presents a variational Bayesian inference algorithm that captures wall clutter and provides channel images as full posterior distributions. Experimental results on real data show that the proposed model is very effective at removing wall clutter and enhancing target localization.

UOW Authors


  •   Tang, Van H. (external author)
  •   Bouzerdoum, Abdesselam (external author)
  •   Phung, Son Lam

Publication Date


  • 2020

Citation


  • V. Tang, A. Bouzerdoum & S. Phung, "A variational Bayesian approach for multichannel through-wall radar imaging with low-rank and sparse priors," in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020, pp. 2523-2527.

Scopus Eid


  • 2-s2.0-85091156108

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4379

Start Page


  • 2523

End Page


  • 2527