In this paper, we propose a new direction of arrival (DOA) estimation algorithm, in which DOA estimation is achieved by finding the sparsest support set of multiple measurement vectors (MMV) in an over-complete dictionary. The proposed algorithm is based on ℓp norm minimization, which belongs to non-convex optimization. Therefore, the quasi-Newton method is used to converge the iterative process. There are two advantages of this algorithm: one is the higher possibility and resolution of distinguishing closely spaced sources, and the other is the adaptive regularization parameter adjustment. Moreover, an accelerating strategy is applied in the computation, and a weighted method of the proposed algorithm is also introduced to improve the accuracy. We conducted experiments to validate the effectiveness of the proposed algorithm. The performance was compared with several popular DOA estimation algorithms and the Cramer–Rao bound (CRB).

No abstract