Open AccessMode‐SVD‐Based Maximum Likelihood Source Localization Using Subspace Approach
Author(s) -
Park CheeHyun,
Hong KwangSeok
Publication year - 2012
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.12.0111.0728
Subject(s) - singular value decomposition , subspace topology , noise (video) , algorithm , mathematics , taylor series , a priori and a posteriori , covariance matrix , inverse , matrix (chemical analysis) , noise measurement , computer science , artificial intelligence , noise reduction , mathematical analysis , philosophy , epistemology , image (mathematics) , materials science , geometry , composite material
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 Cramér‐Rao lower bound.