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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2007, 12(1), 1-9; https://doi.org/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
PDF [159 KB, uploaded 30 March 2016]

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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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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