Blind Signal Separation: Statistical Principles

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Blind Signal Separation: Statistical Principles (2003)

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by Jean-Francois Cardoso

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528 - 4 self

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@MISC{Cardoso03blindsignal,
    author = {Jean-Francois Cardoso},
    title = {Blind Signal Separation: Statistical Principles},
    year = {2003}
}

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Abstract

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics. The objective of this paper is to review some of the approaches that have been recently developed to address this exciting problem, to show how they stem from basic principles and how they relate to each other.

Keyphrases

blind signal separation statistical principle familiar second order statistic data analysis mutual independence unobserved signal powerful approach array processing basic principle exciting problem independent component analysis observed mixture

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