A Novel STAP Based on Spectrum-Aided Reduced-Dimension Clutter Sparse Recovery

Abstract

Space-time adaptive processing based on clutter sparse recovery (SR-STAP) methods outperform traditional statistical-STAP algorithms in scenarios with limited training numbers. However, the computational burden of current SR-STAP methods is extremely heavy, particularly when the number of discretized angle and Doppler grid points is large, which hinders these methods from coming into practical use. This letter proposes a spectrum-aided reduced-dimension SR-STAP method to overcome this issue. The proposed method employs the clutter spectrum estimated by training samples to design the reduced-dimension dictionary. By solving a reduced-dimension sparse recovery problem, the computational load of the proposed method can be reduced significantly while only slightly degrading the performance of clutter suppression and target detection compared with current SR-STAP methods. Numerical experiments using both simulated and measured data validate the effectiveness of the proposed method.