Joint DOA and Clutter Covariance Matrix Estimation in Compressive Sensing MIMO Radar

Abstract

We apply the technique of compressive sensing (CS) to multiple-input multiple-output (MIMO) radars to estimate the direction of arrival (DOA) of potential targets embedded in cluttered environments using far fewer samples than the Nyquist rate. Specifically, incorporating clutter into the sparse Bayesian learning (SBL) methodology, we devise a novel algorithm for joint estimation of targets' DOAs, clutter covariance matrix, and noise variance. Furthermore, we develop a low-complexity and fast version of the proposed algorithm, which can be efficiently employed in practice.