Source separation: models, concepts, algorithms and performances

Abstract : Blind source separation is now often considered as a means to exploit the spatial diversity in antenna array processing when source signals and array response are unknown, yielding more powerful processing schemes in digital communications, radar, and sonar. Another instance is the so-called Independent Component Analysis (ICA), which can be viewed a a general-purpose tool taking the place of the Principal Component Analysis (PCA), thus being applicable in a wide range of problems, including data analysis. Instances of this versatile framework are subsequently pointed out. This chapter is a survey of the problem, encompassing algebraic and statistical tools as well as concrete numerical algorithms, and performance analysis. It includes a thorough bibliographical state of the art. Improvements to be carefully studied include, in particular, the ability to detect and extract more sources than sensors.
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Chapitre d'ouvrage
S. Haykin. Unsupervised Adaptive Filtering, volume 1 : Blind Source Separation, Wiley, pp.191--236, 2000, Series on Adaptive and Learning Systems for Communications, Signal Processing and Control
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https://hal.archives-ouvertes.fr/hal-00169604
Contributeur : Pierre Comon <>
Soumis le : mardi 4 septembre 2007 - 14:28:10
Dernière modification le : mercredi 21 janvier 2009 - 15:44:48

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  • HAL Id : hal-00169604, version 1

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Pierre Comon, Pascal Chevalier. Source separation: models, concepts, algorithms and performances. S. Haykin. Unsupervised Adaptive Filtering, volume 1 : Blind Source Separation, Wiley, pp.191--236, 2000, Series on Adaptive and Learning Systems for Communications, Signal Processing and Control. <hal-00169604>

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