Robust sparse Bayesian learning STAP method for discrete interference suppression in nonhomogeneous clutter

Abstract

Conventional space-time adaptive processing (STAP) methods would suffer severely performance loss in complex clutter environment of airborne phased array radar, especially when discrete interference is in the range cell under test (CUT). In order to improve the discrete interference suppression in practical complex clutter, a robust sparse Bayesian learning (SBL) STAP method is proposed in this paper. In the proposed method, the estimation of the space-time spectral distribution and the calibration of overcomplete dictionary are achieved iteratively. The spectral profiles of the clutter and discrete interference is estimated based on maximum a posteriori (MAP) principle, the mismatch of overcomplete dictionary is calibrated by the cost function minimization. Because of the robust high-resolution sparse recovery of the clutter and discrete interference profiles, the proposed method cannot only effectively eliminate the discrete interference, but also suppress the clutter component with small number of training data. Through the simulated and actual airborne phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in nonhomogeneous environment.