• DocumentCode
    1971110
  • Title

    Source Extraction Using Novel NonGaussianity Measure

  • Author

    Liu, Keying ; Li, Rui

  • Author_Institution
    Dept. of Math., North China Univ. of Water Resources & Electr. Power, Zhengzhou, China
  • fYear
    2010
  • fDate
    22-23 June 2010
  • Firstpage
    396
  • Lastpage
    399
  • Abstract
    The purpose of this paper is to develop novel Blind Source Extraction (BSE) algorithms from linear mixtures of the statistically dependent source signals. we show that maximization of the non Gaussianity (NG) measure can not only separate the statistically independent but also dependent source signals. The NG measure is defined by statistical distances between distributions based on the cumulative density function instead of traditional probability density function which can be estimated by the order statistics efficiently. The NG distance provide new cost function whose maximization performs the extraction of one dependent component at each successive stage of a delation procedure using an iterative algorithm.
  • Keywords
    blind source separation; statistical analysis; blind source extraction; cost function; cumulative density function; dependent source signals; nongaussianity measure; order statistics; probability density function; statistical distances; Algorithm design and analysis; Analytical models; Density measurement; Estimation; Signal processing algorithms; Source separation; Blind Source Extraction (BSE); Blind Source Separation (BSS); Dependent Component Analysis (DCA); Independent Component Analysis (ICA); NonGaussianity (NG); Order Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4244-6640-5
  • Electronic_ISBN
    978-1-4244-6641-2
  • Type

    conf

  • DOI
    10.1109/ICICCI.2010.78
  • Filename
    5565951