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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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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)
  • Received : 2011.11.21
  • Accepted : 2012.04.06
  • Published : 2012.10.31
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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.

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