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

• Published in: ETRI journal Vol. 34; no. 5; pp. 684 - 689
• Main Authors: Park, Chee‐Hyun, Hong, Kwang‐Seok
• Format: Journal Article
• Language: English
• Published: 한국전자통신연구원 01.10.2012
• Subjects: localization; maximum likelihood; Singular value decomposition; TOA; weight; 전자/정보통신공학
• ISSN: 1225-6463; 2233-7326; 2233-7326
• DOI: 10.4218/etrij.12.0111.0728

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.