A low-rank and jointly-sparse approach for multipolarization through-wall radar imaging

Citation data:

2017 IEEE Radar Conference, RadarConf 2017, Page: 0263-0268

Publication Year:
2017
Captures 3
Readers 3
Citations 2
Citation Indexes 2
Repository URL:
https://ro.uow.edu.au/eispapers1/370
DOI:
10.1109/radar.2017.7944209
Author(s):
Bouzerdoum, Abdesselam; Tang, Van Ha; Phung, Son Lam
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Computer Science; Physics and Astronomy; Engineering; Science and Technology Studies
conference paper description
This paper presents a low-rank and jointly-sparse approach for imaging stationary targets using multipolarization through-wall radar (TWR). The proposed approach exploits two important characteristics of multichannel TWR signals: low-rank structure of the wall reflections and jointly-sparse structure of the polarization images. The task of removing wall reflections and reconstructing multichannel images of the same scene behind-the-wall is formulated as a regularized least squares optimization problem, where the low-rank regularization is imposed on the wall returns and the joint-sparsity constraint is enforced on the multichannel images. An iterative algorithm is introduced to solve the optimization problem, yielding multichannel images of the indoor targets. Experimental results on real radar data show that the proposed model enhances multichannel imaging in terms of target-to-clutter ratio and indoor target localization.