Sparse Bayesian DOA Estimation Based on Subspace Model

A direction of arrival (DOA) estimation algorithm based on a subspace model using sparse Bayesian learning (SBL) is proposed to improve the accuracy of the DOA estimation under coherent source conditions. First, the subspace smoothing (SS) technique was integrated with the weighted subspace fitting technique. This subspace model was then utilized in the SBL algorithm, the discrete grid points were considered as dynamic parameters and the grid point positions were updated iteratively using the expectation-maximization (EM) algorithm. Compared with traditional sparse algorithms, the proposed algorithm has significant advantages in terms of root mean square error and computational complexity, and has strong robustness to the estimation error of the number of signal sources.