DocumentCode :
3539852
Title :
Blind separation of dependent sources using Schweizer-Wolff measure
Author :
Liu, Keying ; Li, Rui ; Wang, Fasong
Author_Institution :
Dept. of Math., North China Univ. of Water Resources & Electr. Power, Zhengzhou, China
fYear :
2012
fDate :
14-15 Aug. 2012
Firstpage :
300
Lastpage :
303
Abstract :
There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.
Keywords :
blind source separation; Schweizer-Wolff measure; blind separation; blind signal separation algorithm; dependent sources; linear dependent source signal; pairwise dependence; Algorithm design and analysis; Analytical models; Correlation; Educational institutions; Independent component analysis; Signal processing algorithms; Source separation; Blind Source Separation (BSS); Dependent Component Analysis (DCA); Independent Component Analysis (ICA); Schweizer-Wolff Measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
Conference_Location :
Jalarta
Print_ISBN :
978-1-4673-1459-6
Type :
conf
DOI :
10.1109/URKE.2012.6319571
Filename :
6319571
Link To Document :
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