A robust KA-STAP method for terrain clutter suppression in hybrid baseline radar systems

Abstract

Suffering from the problem of clutter non-homogeneity, the terrain clutter suppression becomes a significant challenge in hybrid baseline radar system. This paper proposed a robust knowledge aided homogeneous sample selection (KAHSS) method based on an iterative clutter covariance matrix (CCM) mismatch correction processing. First, the initial training samples are selected by prior clutter data that constructed by using the prior knowledge of digital elevation model (DEM). Then, an iterative CCM mismatch correction is performed to select and update the homogeneous samples. Essentially, KAHSS was designed to compensate the estimation bias due to system errors and inaccurate prior knowledge. This allows training samples with similar clutter properties can be selected, and accordingly improves the robustness of CCM estimation. Finally, the experimental results demonstrate that the proposed method can obtain better clutter suppression performance than other contrast methods.