LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES

DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform linear array.