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http://dx.doi.org/10.4218/etrij.12.0111.0728

Mode-SVD-Based Maximum Likelihood Source Localization Using Subspace Approach  

Park, Chee-Hyun (Department of Electrical and Computer Engineering, University of Wisconsin Madison)
Hong, Kwang-Seok (School of Information and Communication Engineering, Sungkyunkwan University)
Publication Information
ETRI Journal / v.34, no.5, 2012 , pp. 684-689 More about this Journal
Abstract
A mode-singular-value-decomposition (SVD) maximum likelihood (ML) estimation procedure is proposed for the source localization problem under an additive measurement error model. In a practical situation, the noise variance is usually unknown. In this paper, we propose an algorithm that does not require the noise covariance matrix as a priori knowledge. In the proposed method, the weight is derived by the inverse of the noise magnitude square in the ML criterion. The performance of the proposed method outperforms that of the existing methods and approximates the Taylor-series ML and Cram$\acute{e}$r-Rao lower bound.
Keywords
Singular value decomposition; maximum likelihood; weight; TOA; localization;
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