2002•Unpublished venueRequires access

Multichannel blind identification with unknown number of sources

Jun Wang, Zhenya He

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Abstract

A trispectra method for solving the m-input n-output (nrm) wideband blind identification and signal separation problem with unknown number of sources m is presented. The method is universal in the sense that it does not impose any restriction on the probability distribution of the input signals provided that they are non-Gaussian. A criterion, which states a sufficient condition for identification and separation, has been proved. An algorithm is also developed based on the criterion, whose efficiency is verified by the simulations.

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What this paper is about

A trispectra method for solving the m-input n-output (nrm) wideband blind identification and signal separation problem with unknown number of sources m is presented. The method is universal in the sense that it does not impose any restriction on the probability distribution of the input signals provided that they are non-Gaussian. A criterion, which states a sufficient condition for identification and separation, has been proved. An algorithm is also developed based on the criterion, whose efficiency is verified by the simulations.

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Available abstract

A trispectra method for solving the m-input n-output (nrm) wideband blind identification and signal separation problem with unknown number of sources m is presented. The method is universal in the sense that it does not impose any restriction on the probability distribution of the input signals provided that they are non-Gaussian. A criterion, which states a sufficient condition for identification and separation, has been proved. An algorithm is also developed based on the criterion, whose efficiency is verified by the simulations.

Key concepts: Blind signal separation, Identification (biology), Gaussian, Wideband, Algorithm, Computer science, Mathematics, SIGNAL (programming language)

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