A computationally efficient direction-of-arrival estimation algorithm, robust against coherent sources and small sample size

Direction-of-arrival (DOA) estimation algorithms usually impose high computation burden and most of them are incapable of facing the coherent sources. In this paper, a new computationally efficient algorithm that is robust against the coherent sources is proposed. This method is based on a combination of the Maximum Likelihood (ML) and Orthogonal-Matching-Pursuit (OMP) algorithms. Simulation results show that the proposed algorithm has good performance in the small sample size cases and the coherency does not affect its performance.