Improved capon estimators for DOD and DOA estimation in large array MIMO radar: A random matrix method

Joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) in bistatic multiple-input-multiple-output (MIMO) radar is a powerful technique for target localization. However, in the scenario of large arrays in which the numbers of array elements and observations are on the same order of magnitude, the sample covariance matrix cannot directly replace the statistical covariance matrix. In this paper, we propose two robust DOA and DOD estimation algorithms based on improved 2D-Capon estimators for MIMO radar with large arrays. The sample covariance matrix of the array received signals is modified by diagonal loading technique, and then the eigenvalues and eigenvectors of the diagonally loaded covariance matrix are accurately estimated utilizing Stieltjes transform and random matrix theory (RMT). Finally, two improved 2D-Capon estimators are derived to obtain robust performance of DOD and DOA estimation. The simulation shows that the improved algorithms can estimate angles more accurately than the traditional 2D-Capon algorithm in large dimension regime.