Improved PRI‐staggered space–time adaptive processing algorithm based on projection approximation subspace tracking subspace technique

Abstract

The pulse repetition interval (PRI)-staggered space–time adaptive processing (STAP) method is difficult to be processed in real time because of the large sample support and the huge computational complexity. The subspace technique can solve the aforementioned problem by exploiting the low rank property of the covariance matrix. Therefore the conventional PRI-staggered STAP method is improved based on the subspace technique in this study. The eigenvalue decomposition technique is firstly introduced into the PRI-staggered STAP method, where only the dominant eigenvectors are applied to construct the clutter subspace so that the sample support requirement is reduced dramatically. However, it turns out to be impractical because of the inherent computational complexity. To deal with the complexity problem, projection approximation subspace tracking as a fast subspace tracking method is applied to modify the conventional PRI-staggered STAP method. The clutter subspace can be approximated by using the concept of projection approximation and the recursive least squares processing, so that both the sample support and computational complexity can be reduced significantly. The performance of the proposed method is demonstrated by using the simulated data and the measured airborne radar data from the multichannel airborne radar measurements database.