Approximate ML direction finding in spatially correlated noise using oblique projections

We consider the problem of maximum likelihood estimation of directions of arrival of multiple source signals in the presence of unknown spatially correlated Gaussian noise. Oblique projections are used to separate the structured noise from the signal and an approximate maximum likelihood solution is derived. The estimates are obtained by maximizing the modified cost function using a nonlinear optimization technique. Numerical simulations are provided to assess the performance of the proposed approach. Simulations include comparison to the stochastic maximum likelihood and to the weighted subspace fitting, as well as to the Cramer-Rao bound.