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On the cramer-rao bound for estimating the mixing matrix in noisy sparse component analysis

Zayyani, H ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/LSP.2008.2003989
  3. Publisher: 2008
  4. Abstract:
  5. In this letter, we address the theoretical limitations in estimating the mixing matrix in noisy sparse component analysis (SCA) for the two-sensor case. We obtain the Cramer-Rao lower bound (CRLB) error estimation of the mixing matrix. Using the Bernouli-Gaussian (BG) sparse distribution, and some simple assumptions, an approximation of the Fisher information matrix (FIM) is calculated. Moreover, this CRLB is compared to some of the main methods of mixing matrix estimation in the literature. © 2008 IEEE
  6. Keywords:
  7. Cramer-rao lower bound ; Error estimations ; Gaussian ; Mixing matrix ; Mixing matrix estimation ; Sparse component analysis ; Sparse distribution ; Apartment houses ; Control theory ; Cramer-rao bounds ; Estimation ; Fisher information matrix ; Mixing ; Signal analysis ; Wireless telecommunication systems ; Blind source separation
  8. Source: IEEE Signal Processing Letters ; Volume 15 , 2008 , Pages 609-612 ; 10709908 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/4639576