Maximum-likelihood DOA estimation by data-supported grid search

After reviewing the main existing methods for determining the maximum-likel ihood (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 likeliho od function (LF) over a set of points derived from the data. We show that t he data-supported grid search of the LF provides a performance similar to t hat achieved by a genetic algorithm (GA), but at a significantly lower comp utational cost. We use an ESPRIT-like algorithm to obtain the grid points w ith support in the data, although our approach is not limited to this choic e.