An optimized EASI algorithm

This paper addresses the problem of blind source separation and presents a kind of optimized equivariant adaptive separation via independence (EASI) algorithms. According to the cumulant based approximation to the mutual information contrast function, the EASI learning rule is optimized by multiplyi...

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Bibliographic Details
Published inSignal processing Vol. 89; no. 3; pp. 333 - 338
Main Authors Ye, Jimin, Jin, Haihong, Lou, Shuntian, You, Kejun
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.03.2009
Elsevier
Subjects
Adaptive algorithms
Algorithms
Applied sciences
Approximation
Blind source separation
Blinds
Cumulant based approximation of contrast function
Detection, estimation, filtering, equalization, prediction
EASI algorithm
Exact sciences and technology
Information, signal and communications theory
Learning
Mathematical analysis
Miscellaneous
Optimization
Separation
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Cumulant based approximation of contrast function
Blind source separation
EASI algorithm
Learning
Performance evaluation
Accuracy
Simulation
Signal processing
Algorithm
Steady state
Cumulant
Mutual information
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Summary:This paper addresses the problem of blind source separation and presents a kind of optimized equivariant adaptive separation via independence (EASI) algorithms. According to the cumulant based approximation to the mutual information contrast function, the EASI learning rule is optimized by multiplying the symmetric part with an optimal time variant weight coefficient. Simulation results show the proposed optimized EASI algorithms outperform the existing algorithms in convergent speed and steady-state accuracy.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2008.08.015