A space time adaptive processing method based knowledge aided generalized SPICE

Abstract:
Space-time adaptive processing (STAP) with finite samples is supposed to be a crucial technique for airborne radar systems. In this letter, a novel STAP method-based knowledge aided (KA) generalized sparse iterative covariance-based estimator (SPICE) is proposed which enhance the clutter suppression performance with finite samples. Firstly, we use the prior knowledge of radar system parameters and surface information to obtain the information of clutter ridge, then we construct the dictionary with the knowledge of the clutter. Secondly, we assign different norm constraints on the clutter and noise parameters in the generalized SPICE instead of identical norm constraints in original SPICE. Numerical experiments prove that the proposed algorithm can achieve great clutter suppression performance with finite samples.