Robust Approach for Blind Source Separation in Non-Gaussian Noise Environments.

In this contribution, we address the issue of Blind Source Separation (BSS) in non-Gaussian noise. We propose a twostep approach by combining the fractional lower order statistics (FLOS) for the mixing matrix estimation and minimum entropy criterion for noise-free source components estimation with the gradient-based BSS algorithms in an elegant way. First, we extend the existing gradient algorithm in order to reduce the bias in the demixing matrix caused by the non-Gaussian noise. In the noise cancellation step, we derive a new kind of nonlinear function that depends on the noise distribution and we discuss the optimal choice of this nonlinearity assuming a generalized Gaussian noise model. The optimal choice, in the minimum entropy sense, is robust against the influence of Gaussian and non-Gaussian noise including heavy-tailed model. The effectiveness and the robustness of the proposed separating algorithm are shown on numerical examples.

Main Author:	Sahmoudi, Mohamed.
Other Authors:	Snoussi, Hichem., Amin, Moeness G.
Format:	Villanova Faculty Authorship
Language:	English
Published:	2006
Online Access:	http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173594