Maximum-likelihood localization of narrow-band autoregressive sources via the EM algorithm

The authors derive an expectation-maximization algorithm for the maximum-likelihood estimation of the directions of arrival of multiple narrowband autoregressive (AR) signals embedded in noise. The proposed algorithm simultaneously estimates the location parameters, the AR coefficients, and the signals. The additional structural information that the sources are of the AR type enables one to resolve the case in which the number of sources is equal to or possibly larger than the number of sensors. The authors provide examples demonstrating that the proposed algorithm can resolve two spectrally and spatially closely spaced sources by using a two-sensor array