Radar Stationary and Moving Indoor Target Localization with Low-rank and Sparse Regularizations

This paper proposes a low-rank and sparse regularized optimization model to address the problem of wall clutter mitigation, stationary, and moving target indications using through-wall radar. The task of wall clutter suppression and target image reconstruction is formulated as a nuclear and ℓ
₁
penalized least squares optimization problem in which the nuclear-norm term enforces for a low-rank wall clutter matrix and the ℓ
₁
-norm term promotes the sparsity of the target images. An iterative algorithm based on the proximal gradient technique is introduced to solve the optimization problem. The solution comprises the wall clutter and images of stationary and moving targets. Experiments are conducted on real radar data under compressive sensing scenarios. The results show that the proposed model is very effective at removing unwanted wall clutter, reconstructing stationary targets, and capturing moving targets.