  In this paper, the problem of determining the number of signal sources
impinging on an array of sensors and estimating their directions-of-arrival
(DOAs) in the presence of spatially white nonuniform noise is considered. It is
known that, in the case of nonuniform noise, the stochastic likelihood function
cannot be concentrated with respect to the diagonal elements of noise
covariance matrix. Therefore, the stochastic maximum-likelihood (SML) DOA
estimation and source enumeration in the presence of nonuniform noise requires
multidimensional search with very high computational complexity. Recently, two
algorithms for estimating noise covariance matrix in the presence of nonuniform
noise have been proposed in the literature. Using these new estimates of noise
covariance matrix, an approach for obtaining the SML estimate of signal DOAs is
proposed. In addition, new approaches are proposed for SML source enumeration
with information criteria in the presence of nonuniform noise. The important
feature of the proposed SML approaches for DOA estimation and source
enumeration is that they have admissible computational complexity. In addition,
some of them are robust against correlation between source signals. The
performance of the proposed DOA estimation and source enumeration approaches
are investigated using computer simulations.
