Linear Regression Based Clutter Reconstruction for STAP

Abstract

Space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in a strong clutter background for airborne phased array radar systems. In this paper, we consider the extremely heterogeneous case, i.e., the number of available training samples is limited to one. The sparse recovery (SR) technique is first utilized to obtaining the independent clutter patches. Contrary to traditional SR STAP which estimates the clutter covariance matrix (CCM) with these clutter patches, the proposed approach will estimate the ‘clutter ridge’ based on linear regression by making use of these clutter patches. With the prior knowledge of number of receiver elements, a more accurate estimation of CCM is obtained. From the simulation results, the proposed approach can achieve a great performance enhancement of clutter suppression with only one training sample compared with conventional SR based STAP algorithms. Even for the cases where amplitude and phase errors are consider, the proposed approach can be superior to traditional SR STAP about 5~10 dB.