Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery

This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to previous approaches where the signal at each antenna location is reconstructed independently. Furthermore, to promote sparsity and improve reconstruction accuracy, a sparsifying wavelet dictionary is employed in the sparse signal recovery. Following signal reconstruction, a subspace projection technique is applied to remove wall clutter, prior to image formation. Experimental results on real data show that the proposed approach produces significantly higher reconstruction accuracy and requires far fewer measurements for forming high-quality images, compared to the single-signal compressed sensing model, where each antenna signal is reconstructed independently.

1. Introduction

Through-the-wall radar imaging (TWRI) is an emerging technology that has attracted considerable research interest due to its numerous civilian and military applications [1]–[3]. In TWRI, the scene behind the wall is first interrogated by transmitting wideband electromagnetic (EM) waves; then, the reflected signals from the wall and targets are processed to form the image. For target detection and localization, the clutter caused by the front wall EM returns must be mitigated prior to image formation since strong wall reflections obscure stationary targets, especially those with small radar-cross-section.