Localization Of Distributed Sources

Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint which is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that localizes the distributed sources by estimating their parameter vector. The method is based on minimizing a scalar product between the array manifold and the noise eigenvectors of the correlation matrix. We study two cases corresponding to completely correlated and totally uncorrelated signal distributions. We compare our method to the conventional MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation. R'esum'e La plupart des algorithmes de traitement d'antenne reposent sur l'hypoth`ese que les signaux re¸cus sont g'en'er'es par des source..