Unconditional maximum likelihood approach for near-field source localization

Localization of near-field sources require sophisticated estimation algorithms. In this paper, we propose unconditional maximum likelihood method for estimating direction of arrival and angle parameters of near-field sources. However, calculation of ML estimation from corresponding likelihood function results in difficult nonlinear constraint optimization problem. We therefore employed Expectation/Maximization iterative method for obtaining ML estimates. The most important feature of the EM algorithm is that it decomposes the observed data into its components and then estimates the parameters of each signal component separately providing computationally efficient solution resulting optimization problem.