STAP Based on Two-Level Block Sparsity

In this paper, to improve the performance of airborne radar clutter suppression in the case of a small number of training samples, a new space-time adaptive processing (STAP) algorithm is proposed by exploiting the two-level block sparsity. In the angle-Doppler domain, the clutter profile usually appears in clustered area and the radar signal at nearby range cells generally have the same sparse structure. The proposed algorithm utilizes both the clustered property and the common sparsity across the adjacent range cells, i.e., the two-level block sparsity, to obtain an accurate estimation of the clutter covariance matrix and therefore improves the STAP performance. Simulation results demonstrate the superiority of the proposed algorithm over existing STAP methods in terms of clutter suppression and detection of low-speed targets with small number of training samples.