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2008•Yunnan Daxue xuebao. Shehui kexue banRequires access

Blind source separation based on information theory

Xiao Min

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Abstract

Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis(ICA) is a new method to solve this problem.The most common way in independent component analysis is the separation based on information theory.FastICA algorithm and nature step algorithm are the main way in it.Some groups of signals were separated.The analysis and simulations suggest that the FastICA algorithm is the best way.

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What this paper is about

Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis(ICA) is a new method to solve this problem.The most common way in independent component analysis is the separation based on information theory.FastICA algorithm and nature step algorithm are the main way in it.Some groups of signals were separated.The analysis and simulations suggest that the FastICA algorithm is the best way.

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Available abstract

Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis(ICA) is a new method to solve this problem.The most common way in independent component analysis is the separation based on information theory.FastICA algorithm and nature step algorithm are the main way in it.Some groups of signals were separated.The analysis and simulations suggest that the FastICA algorithm is the best way.

Key concepts: FastICA, Independent component analysis, Blind signal separation, Component analysis, Source separation, Component (thermodynamics), Separation (statistics), Computer science

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