The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To\nsolve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of\nthe most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the\nmultidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses\nthe issue of reducing computational complexity of SML estimation ofDOAbased on theAlternatingMinimization (AM) algorithm.\nWe have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an\nefficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter\nwhile the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion\n(IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of\nSML estimation greatly, while IAMis the best. Another advantage of IAMis that this algorithm can avoid the numerical instability\nproblem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.
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