Through-the-Wall Radar Imaging Based on Bayesian Compressive Sensing Exploiting Multipath and Target Structure

Compressive sensing (CS) applied to through-the-wall radar imaging (TWRI) exploits the group sparsity of a target scene in the presence of wall clutter and multipath from enclosed structures towards achieving high-resolution imaging with limited measurements. In this paper, we extend the CS-based TWRI to include the clustering structure property of the target within a hierarchical Bayesian framework. An extended structured spike-and-slab prior is imposed to statistically encourage spatially extended cluster structures of a target scene and model the signal group sparsity due to multipath propagation. The expectation propagation scheme is used for the approximate posterior inference. The proposed nonparametric Bayesian algorithm can achieve substantial improvements in terms of preserving a target cluster structure and suppressing isolated spurious false alarms compared to other state-of-the-art algorithms. Furthermore, it does not require prior information about the targets themselves, such as size, shape or number.