Multichannel blind identification with unknown number of sources
Jun Wang, Zhenya He
Abstract
Jun Wang, Zhenya He
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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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)