TY - GEN
T1 - A variational Bayesian approach for multichannel through-wall radar imaging with low-rank and sparse priors
AU - Tang, Van Ha
AU - Bouzerdoum, Abdesselam
AU - Phung, Son Lam
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Sparse Bayesian learning
KW - Through-the-wall radar imaging
KW - Variational inference
KW - Wall clutter mitigation
UR - http://www.scopus.com/inward/record.url?scp=85091156108&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9054515
DO - 10.1109/ICASSP40776.2020.9054515
M3 - Conference contribution
AN - SCOPUS:85091156108
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2523
EP - 2527
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -