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
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;
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
DOI :
10.1109/ICICCI.2010.78