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Math. Comput. Appl. 2007, 12(1), 1-9; doi:10.3390/mca12010001

Multichannel Blind Deconvolution Using a Generalized Gaussian Source Model

1
Dept. of System and Bioengineering, Faculty of Engineering, Cairo University, Egypt
2
Dept. of Mathematics, Faculty of Science, Zagazig University, Egypt
*
Authors to whom correspondence should be addressed.
Published: 1 April 2007
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Abstract

In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to un-known sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized gaussian source model.
We use a state space representation to model the mixer and demixer respectively, and show how the parameters of the demixer can be adapted using a gradient descent algorithm incorporating the natural gradient extension. We also present a learning method for the unknown parameters of the generalized Gaussian source model. The
performance of the proposed generalized Gaussian source model on a typical example is compared with those of other algorithm, viz the switching nonlinearity algorithm
proposed by Lee et al. [8].
Keywords: blind deconvolution; blind source separation; generalized Gaussian source model; multichannel blind deconvolution blind deconvolution; blind source separation; generalized Gaussian source model; multichannel blind deconvolution
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Abu-Taleb, A.S.; Zayed, E.M.E.; El-Sayed, W.M.; Badawy, A.M.; Mohammed, O.A. Multichannel Blind Deconvolution Using a Generalized Gaussian Source Model. Math. Comput. Appl. 2007, 12, 1-9.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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