Robust non‐homogeneity detector based on reweighted adaptive power residue

Abstract

Space time adaptive processing (STAP) is an excellent technique for improving ground moving target detection performance of airborne radar. However, covariance matrix estimation required by STAP is commonly corrupted by the presence of target-like signals (i.e. outliers), and thus resulting severe performance degradation. To overcome this problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed, where an adaptively reweighted scheme is employed to training data set. Therefore, the deleterious effect of outliers on the covariance estimation is eliminated and the robustness of the non-homogeneity detector is guaranteed. Performance analysis using the simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the radar detection performance in a dense target environment.