A Recursive Angle-Doppler Channel Selection Method for Reduced-Dimension Space-Time Adaptive Processing

Abstract

This article considers the problem of target detection for applications with limited training samples. Dimensionality reduction in the angle-Doppler domain is considered. An algorithm for selecting the best angle-Doppler channels that maximize the output signal-to-clutter-plus-noise ratio in space-time adaptive processing (STAP) is proposed. Compared with several existing STAP methods, the proposed method can achieve better clutter suppression performance at lower computational complexities when the degrees of freedom of the STAP system are fixed. Moreover, the proposed method can help address the issue of training data shortage, which may be particularly attractive for heterogeneous scenarios. Simulations are conducted for validating the proposed method and demonstrating their high performance.