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This paper presents an efficient method for computing maximum likelihood (ML)direction of arrival (DOA) estimates assuming unknown sensor noise powers. Themethod combines efficient Alternate Projection (AP) procedures with Newtoniterations. The efficiency of the method lies in the fact that all itsintermediate steps have low complexity. The main contribution of this paper isthe method's last step, in which a concentrated cost function is maximized inboth the DOAs and noise powers in a few iterations through a Newton procedure.This step has low complexity because it employs closed-form expressions of thecost function's gradients and Hessians, which are presented in the paper. Themethod's total computational burden is of just a few mega-flops in typicalcases. We present the method for the deterministic and stochastic MLestimators. An analysis of the deterministic ML cost function's gradientreveals an unexpected drawback of its associated estimator: if the noise powersare unknown, then it is either degenerate or inconsistent. The root-mean-square(RMS) error performance and computational burden of the method are assessednumerically.

Efficient ML Direction of Arrival Estimation assuming Unknown Sensor Noise Powers