Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

Abstract

To further improve the efficiency of sparse Bayesian learning (SBL) for direction of arrival (DOA) estimation, a real-valued (unitary) formulation of covariance vector-based relevance vector machine (CV-RVM) technique is proposed in this paper. The covariance matrix of the sensor output is firstly transformed into a real-valued covariance matrix via unitary transformation, and the real-valued covariance matrix can be sparsely represented in a real-valued over-complete dictionary. Then the sparse Bayesian learning technique implemented in real domain is used to estimate the DOA. According to the property of the real-valued covariance matrix, unitary single measurement vector (USMV) CV-RVM for uncorrelated signals and unitary multiple measurement vector (UMMV) CV-RVM for correlated signals are developed, respectively. Due to the fact that the proposed methods are implemented in real domain and the snapshots are doubled via unitary transformation, the proposed methods have lower computational cost and better performance compared to the original SMV CV-RVM and MMV CV-RVM. Simulation results show the effectiveness of the proposed methods.