DOD and DOA estimation for bistatic MIMO radars with sparse Bayesian learning

A sparse Bayesian learning (SBL) based method with a novel grid deriving strategy is proposed in this paper for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radars. Directly applying compressed sensing methods to MIMO radars leads to a heavy computational load because of high dimensional matrix operations. To solve this problem and improve the estimation accuracy, we first construct a coarse grid with some proper initializations, and then resorts to an off-grid SBL model to handle the off-grid gap, where an expectation-maximization (EM) algorithm is utilized iteratively for grid refining aiming to narrow the gap between the true and the estimated DOD and DOA. Simulation results verify the efficiency of the proposed method.