Through the Wall Scene Reconstruction Using Low Rank and Total Variation

In through-the-wall radar imaging, wall clutter mitigation and image formation are often solved separately, resulting in a suboptimal solution. This paper presents a scene reconstruction model with low rank, sparsity, and total-variation constraints that simultaneously remove the wall clutter and form the image of the scene. The proposed method exploits the low rank property of the wall clutter to remove the wall return and imposes sparsity and total variation constraints to suppress the background clutter and noise in the image. An alternating direction technique is developed to optimize the proposed model. Experimental results show that the proposed method produces images with better target to clutter ratios than delay and sum beamforming in conjunction with wall clutter mitigation and the existing low-rank and joint sparse method.