Underdetermined DOA estimation using coprime array via multiple measurement sparse Bayesian learning

Underdetermined direction of arrival (DOA) estimation with coprime array is discussed in the framework of multiple measurement sparse Bayesian learning (MSBL). Exploiting the extended difference coarray, a larger number of degrees of freedom can be obtained for locating more sources than sensors. A linear operation and a prewhitening procedure are incorporated into the sparse signal recovery model to eliminate the influence of noise. Then, MSBL employs an empirical Bayesian strategy to resolve l0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_{0}$$\end{document} minimization problem. Simulation results show the superiority of the MSBL algorithm in underdetermined DOA detection performance, resolution ability and estimation accuracy when there are multiple measurement vectors for on-grid and off-grid sources, respectively.