Robust generalized inner products algorithm using prolate spheroidal wave functions

Abstract

The estimated covariance matrix is corrupted by the interference-target signals (outliers) in nonhomogeneous clutter environments, which leads the conventional space-time adaptive processing (STAP) to be degraded significantly in clutter suppression. Therefore, a robust generalized inner products (GIP) algorithm by utilizing prolate spheroidal wave functions (PSWF) is proposed to eliminate the outliers from the training samples set in this paper. In the proposed method (PSWF-GIP), the clutter covariance matrix of the range under test is constructed based on the PSWF which are computed off-line and stored in the memory beforehand. In the following, the constructed covariance matrix is combined with the conventional GIP method to eliminate the training samples contaminated by the outliers in the training samples set. Comparing with the conventional GIP method, the simulation results show that the PSWF-GIP method can more effectively eliminate the outliers and improve the performance of STAP in nonhomogeneous clutter environments.