Radar imaging of stationary indoor targets using joint low-rank and sparsity constraints

Abstract

This paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated as a joint low-rank and sparsity constrained optimization problem. Here, the low rank constraint is imposed on the wall component and the sparsity constraint is used to model the target component. An iterative soft thresholding algorithm is developed to estimate a low-rank matrix of wall clutter and a sparse matrix of target reflections from a reduced measurement set. Once the wall and target components are estimated, the target signals are used for scene reconstruction. Experimental evaluation was conducted using real radar data. The results show that the proposed model is very effective at removing wall clutter and reconstructing the image of behind-thewall targets from reduced measurements. ABSTRACTThis paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated as a joint low-rank and sparsity constrained optimization problem. Here, the low rank constraint is imposed on the wall component and the sparsity constraint is used to model the target component. An iterative soft thresholding algorithm is developed to estimate a low-rank matrix of wall clutter and a sparse matrix of target reflections from a reduced measurement set. Once the wall and target components are estimated, the target signals are used for scene reconstruction. Experimental evaluation was conducted using real radar data. The results show that the proposed model is very effective at removing wall clutter and reconstructing the image of behind-the-wall targets from reduced measurements.

Clutter Reduction and Target Tracking in Through-the-Wall Radar

“…One can notice that our method produces slightly lower TCR than that of background subtraction method. On the other hand, our method clearly detected two targets, and the iterative soft thresholding approach [10] and SVD method [7] both missed the second target, shown in Fig. 10(b) and (d).…”

“…9(a). For a comparison purpose, the results of iterative soft thresholding method [10], background subtraction [1] and SVD method [7] are depicted in Fig. 9(b) to (d).…”

“…In Fig. 9(b), the image obtained by approach in [10] that is based on low-rank and sparse model has a slight tailing. Although background subtraction yields a higher TCR, its performance heavily relies on the availability of the empty scene, which is not possible in real applications.…”

“…In recent studies, two observations are revealed that the front wall reflections present a low-rank property, while the target returns present a sparse property. Therefore, the joint low-rank and sparse approaches for clutter reduction and scene reconstruction have been developed based on compressed sensing (CS) technique [10]- [17]. Particularly, in [10]- [12], Tang et al used an iterative soft threshold method to solve the joint optimization problem, while in [15], the optimization problem was solved utilizing the mathematical toolbox TFOCS [18].…”

“…The results of the one target scene: (a) image by the proposed method, (b) image by Tang's method[10], (c) image by background subtraction[1], (d) image by SVD method[7].Two target scene, TCR=11.2768 dB…”