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Kurtosis approach nonlinear blind source seperation

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dc.contributor.author Duong, Vu A.
dc.contributor.author Stubbemd, Allen R.
dc.date.accessioned 2009-07-08T16:15:35Z
dc.date.available 2009-07-08T16:15:35Z
dc.date.issued 2005-12-14
dc.identifier.citation InTech '05, Phu Ket, Thailand, December 14-16, 2005. en_US
dc.identifier.clearanceno 05-2776
dc.identifier.uri http://hdl.handle.net/2014/41344
dc.description.abstract In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2005. en_US
dc.subject independent component analysis
dc.subject kurtosis
dc.subject higher order statistics
dc.title Kurtosis approach nonlinear blind source seperation en_US
dc.type Preprint en_US


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