Maximum-likelihood DOA estimation by data-supported grid search

After reviewing the main existing methods for determining the maximum-likelihood (ML) estimates of the direction-of-arrival (DOA) parameters in array signal processing applications, we introduce a new conceptually simple and computationally effective approach that consists of maximizing the likelihood function (LF) over a set of points derived from the data. We show that the data-supported grid search of the LF provides a performance similar to that achieved by a genetic algorithm (GA), but at a significantly lower computational cost. We use an ESPRIT-like algorithm to obtain the grid points with support in the data, although our approach is not limited to this choice.