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A Matrix Completion Approach for Wall-Clutter Mitigation in Compressive Radar Imaging of Indoor Targets

Van Ha, T. and Bouterdoum, A. and Phung, S.L. (2018) A Matrix Completion Approach for Wall-Clutter Mitigation in Compressive Radar Imaging of Indoor Targets. In: 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, 15 April 2018 through 20 April 2018.

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Abstract

This paper presents a low-rank matrix completion approach to tackle the problem of wall clutter mitigation for through-wall radar imaging in the compressive sensing context. In particular, the task of wall clutter removal is reformulated as a matrix completion problem in which a low-rank matrix containing wall clutter is reconstructed from compressive measurements. The proposed model regularizes the low-rank prior of the wall-clutter matrix via the nuclear norm, casting the wall-clutter mitigation task as a nuclear-norm penalized least squares problem. To solve this optimization problem, an iterative algorithm based on the proximal gradient technique is introduced. Experiments on simulated full-wave electromagnetic data are conducted under compressive sensing scenarios. The results show that the proposed matrix completion approach is very effective at suppressing unwanted wall clutter and enhancing the targets. © 2018 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/ICASSP.2018.8462000
Additional Information: Conference code: 139797. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9527

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