Multichannel Blind Deconvolution Using a Generalized Gaussian Source Model

A. S. Abu-Taleb, E. M. E. Zayed, W. M. El-Sayed, A. M. Badawy, O. A. Mohammed
2007 Mathematical and Computational Applications  
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
more » ... nt 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] .
doi:10.3390/mca12010001 fatcat:5rigivm3unck5csdwcslr4iwhy