Blind source separation methods based on output nonlinear correlation for bilinear mixtures of an arbitrary number of possibly correlated signals
ID:152 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:382 Oral Presentation

Start Time:2020-06-08 14:20(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS14Dependent Source Separation

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Abstract
Traditional Blind Source Separation (BSS) methods require quite restrictive properties from the source signals (typically, statistically independent or at least uncorrelated sources) and mixing transform (typically, linear instantaneous mixtures). In this paper, we address a much more complex case, where the considered deterministic source signals may be correlated (we only request the source vectors and some associated vectors to be linearly independent) and where the mixing transform is nonlinear (more precisely, bilinear, which is e.g. of high interest for Earth observation applications). We propose a separation principle leading to a new set of BSS algorithms applicable to an arbitrary number of sources and based on nonlinear correlation parameters. Moreover, we analyze the separability properties of this approach and thus show that it is guaranteed to separate the sources up to the trivial scale and permutation indeterminacies.
Keywords
Blind source separation; Dependent sources; Bilinear mixtures; Product proportionality separation principle
Speaker
Yannick Deville
University of Toulouse, France

Submission Author
Yannick Deville University of Toulouse, France
Shahram Hosseini University of Toulouse / CNRS / IRAP, France
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  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

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  • Apr 15 2020

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  • Dec 31 2020

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