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

Conference Paper


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.

Publication Date


  • 2018

Citation


  • V. Tang, A. Bouzerdoum & S. Phung, "A Matrix Completion Approach for Wall-Clutter Mitigation in Compressive Radar Imaging of Indoor Targets," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : Proceedings, 2018, pp. 1608-1612.

Scopus Eid


  • 2-s2.0-85054289957

Start Page


  • 1608

End Page


  • 1612

Place Of Publication


  • United States

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.

Publication Date


  • 2018

Citation


  • V. Tang, A. Bouzerdoum & S. Phung, "A Matrix Completion Approach for Wall-Clutter Mitigation in Compressive Radar Imaging of Indoor Targets," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : Proceedings, 2018, pp. 1608-1612.

Scopus Eid


  • 2-s2.0-85054289957

Start Page


  • 1608

End Page


  • 1612

Place Of Publication


  • United States