Maximum Likelihood DOA Estimation using a Clustering Based Hybrid Optimization Algorithm

A clustering based hybrid optimization algorithm is proposed to solve the optimization problem generated by maximum likelihood method applied to direction of arrival (DOA) estimation of multiple sources. Firstly, the mid-point clustering algorithm divides the multimodal likelihood function into different niches, with each niche containing a local extremum. Then the particle swarm optimization (PSO) algorithm is used to perform the preliminary optimization, and the optimization results are further cast as the initial values for the Levenberg Marquardt (LM) algorithm to obtain the DOAs of sources. Finally, simulations are carried out and the results show that the estimation error of the proposed method is smaller than that of conventional PSO algorithm and Bat (BA) algorithm.