Nonlinear adaptive blind source separation based on kernel function

Authors:
Feng Liu;Cao Zhexin;Qiang Zhi;Shaoqian Li;Min Liang
Affiliations:
National EW laboratory, Chengdu, Sichuan, P.R. China;Jinhua College of profession and technology, Jinhua, Zhejiang, P.R. China;China Electronics technology Group Corporation No.29 Research Institute, Chengdu, Sichuan, P.R.China;University Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China;China Electronics technology Group Corporation No.29 Research Institute, Chengdu, Sichuan, P.R.China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

As the linear method is difficult to recover the sources from the nonlinear mixture signals, in this paper a new nonlinear adaptive blind signal separation algorithm based kernel space is proposed for general invertible nonlinearities. The received mixture signals are mapped from low dimensional space to high dimensional kernel feature space. In the feature space, the received signals form a smaller submanifold, and an orthonormal basis of the submanifold is constructed in this space, as the same time, the mixture signals are parameterized by the basis in the high dimensional kernel space. In the noiseless or noisy situation, the sources are rebuilt online processing by M-EASI and subspace tracking. The results of computer simulations are also presented.