Space-Time Adaptive Processing Based on Weighted Regularized Sparse Recovery

In this paper, novel space-time adaptive processing algorithms based on sparse recovery (SR-STAP) that utilize weighted -norm penalty are proposed to further enforce the sparsity and approximate the original -norm. Because the amplitudes of the clutter components from different snapshots are random variables, we design the corresponding weights according to two different ways, i.e., the Capon's spectrum using limited snapshots and the Fourier spectrum using the current snapshot. Moreover, we apply the weighted idea to both the direct data domain (D3) SR-STAP and SR-STAP using multiple snapshots from adjacent target-free range bins. Simulation results illustrate that our proposed algorithms outperform the existing SR-STAP and D3SR-STAP algorithms.