Blind source separation based on generalized variance

Authors:
Gaoming Huang;Luxi Yang;Zhenya He
Affiliations:
Naval University of Engineering, Wuhan, China;Department of Radio Engineering, Southeast University, China;Department of Radio Engineering, Southeast University, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, a novel blind source separation (BSS) algorithm based on generalized variance is proposed according to the property of multivariable statistical analysis. This separation contrast function of this algorithm is based on second order moments. It can complete the blind separation of supergaussian and subgaussian signals at the same time without adjusting the learning function The restriction of this algorithm is not too much and the computation burden is light. Simulation results confirm that the algorithm is statistically efficient for all practical purpose and the separation effect is very feasible.