Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of expectation–maximization for solving ML direction finding problem stochastic sources, which may be correlated, unknown nonuniform noise. Unlike alternating maximization, ECME algorithm updates both source noise covariance matrix estimates by explicit formulas, can guarantee that are positive semi-definite definite, respectively. Thus, is computationally efficient operationally stable. Simulation results confirm efficiently obtain based DOA estimate each source.