Parametric Localization of Di&ributed 'Sources

Abstmet-ost array pmcsssing algorithms are based on the assumption that the signals are generated by point munxs. Tbis is a mathematical constraint that is not sattpeed in mnny applications In thh paper, we consider situations where the sources are distributed in space with a parametric angular cm-correlation kernel. We propose an algorithm that estimates the parameters of thh model using a generalization of the MUSIC algorithm. The method involves maxhizhg a cost f'unction that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatlal correlation fhction for a miformly Wbuted signal is derived. From thls, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed SOUCC~S. It is also shown that the threshold signal-to-noise ratio required for mlvisg two closely spaced distributed sources is considerably smaller for the new method. I