Maximum Likelihood DOA Estimation in Wireless Sensor Networks Using Comprehensive Learning Particle Swarm Optimization Algorithm

Abstract

Direction of arrival (DOA) estimation is one of the challenging problem in wireless sensor networks. Several methods based on maximum likelihood (ML) criteria have been established in literature. Generally, to obtain the ML solutions, the DOAs must be estimated by optimizing a complicated nonlinear multimodal function over a high-dimensional problem space. Comprehensive learning particle swarm optimization (CLPSO) based solution is proposed here to compute the ML functions and explore the potential of superior performances over traditional PSO algorithm. Simulation results confirms that the CLPSO-ML estimator is significantly giving better performance compared to conventional method like MUSIC in various scenarios at less computational costs.