2021-12-08T03:33:57Zhttps://kitami-it.repo.nii.ac.jp/oaioai:kitami-it.repo.nii.ac.jp:000085532021-09-07T05:06:19Z1Efficient AM Algorithms for Stochastic ML Estimation of DOAChen, HaihuaLi, ShibaoLiu, JianhangZhou, YiqingSuzuki, MasakiyoThe estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.journal articleHindawi Publishing Corporation2016application/pdfInternational Journal of Antennas and Propagation20161101687-5869https://kitami-it.repo.nii.ac.jp/record/8553/files/4926496.pdfenghttp://dx.doi.org/10.1155/2016/4926496Copyright © 2016 Haihua Chen et al.