Maximum likelihood estimation for direction of arrival using a nonlinear optimising neural network

A neural network-based system for a maximum-likelihood estimation of directions of arrival is described. A novel analog neural network implementation of the maximum-likelihood algorithm is presented. Properties of the neural network are discussed with respect to stability and convergence. The performance and behavioral simulations of the network´s dynamics are presented. The results show significant improvement over traditional signal-estimation algorithms, such as MUSIC