Maximum Likelihood DOA Estimator based on Grid Hill Climbing Method

The maximum likelihood estimator for direction of arrival (DOA) possesses optimum theoretical performance as well as high computational complexity. Taking the estimation as an optimization problem of high-dimension nonlinear function, a novel algorithm has been proposed to reduce the computational load of that. At the beginning, the beamforming method is adopted to estimate the spatial spectrum roughly, and a group of initial solutions that obey the law of the “pre-estimated distribution ” are obtained according to the information of the spatial spectrum, and the initial sulotions will locate in the local attractive area of the global optimum solution in great probability. Then, one of the soultions in this group who possesses the maximum fitness is selected to be the initial point of the local search. Grid Hill-climbing Method (GHCM) is a kinds of local search methods that takes a grid as a search unit, which is an improved version of the traditional Hill-climbing Method, and the GHCM is more efficient and stable than the traditional one, so it is adopted to obtain the global optimum solution at last. The proposed algorithm can obtain accurate DOA estimation with lower computational cost, and the simulation shows that the propoesd algorithm is more efficient than the maximum likelihood DOA estimator based on PSO .