Through-The-Wall Moving Target Detection And Localization Using Sparse Regularization

In this paper, we consider moving target detection and localization inside enclosed structures for through-the-wall radar imaging and urban sensing applications. We exploit the fact that the through-the-wall scene is sparse in the Doppler domain, on account of the presence of a few moving targets in an otherwise stationary background. The sparsity property is used to achieve efficient joint range-crossrange-Doppler estimation of moving targets inside buildings using compressive sensing. We establish an appropriate signal model that permits formulation of linear modeling with sensing matrices, so as to achieve scene reconstruction via sparse regularization. Supporting simulation results show that a sizable reduction in the data volume is achieved using the proposed approach without a degradation in system performance.