A Novel Knowledge-aided Approach for Training Data Selection

摘要

This paper proposes a novel knowledge-aided approach for selecting training data in space-time adaptive processing (STAP) whose performance suffers from a severe degradation in heterogeneous interference environment. The proposed approach exploits distances between interference covariance matrices of training data and tested data as the measurements of interference statistical similarities, which helps us gain a deeper insight into the statistics from the point of geometry. Three distances including Euclidean distance, Riemannian distance and a physical distance are combined to distinguish various heterogeneous phenomenons. A prior knowledge is employed in estimating the interference covariance matrices of both training data and tested data. Simulation results illustrate the effectiveness of the proposed approach.