Compressive Radar Imaging of Stationary Indoor Targets with Low-Rank plus Jointly Sparse and Total Variation Regularizations

This paper addresses the problem of wall clutter mitigation and image reconstruction for through-wall radar imaging (TWRI) of stationary targets by seeking a model that incorporates low-rank (LR), joint sparsity (JS), and total variation (TV) regularizers. The motivation of the proposed model is that LR regularizer captures the low-dimensional structure of wall clutter; JS guarantees a small fraction of target occupancy and the similarity of sparsity profile among channel images; TV regularizer promotes the spatial continuity of target regions and mitigates background noise. The task of wall clutter mitigation and target image reconstruction is formulated as an optimization problem comprising LR, JS, and TV regularization terms. To handle this problem efficiently, an iterative algorithm based on the forward-backward proximal gradient splitting technique is introduced, which captures wall clutter and yields target images simultaneously. Extensive experiments are conducted on real radar data under compressive sensing scenarios. The results show that the proposed model enhances target localization and clutter mitigation even when radar measurements are significantly reduced.