Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Abstract

Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method.