Nonlinear Innovation to Noisy Blind Source Separation Based on Gaussian Moments

Authors:
Hongjuan Zhang;Chonghui Guo;Zhenwei Shi;Enmin Feng
Affiliations:
Department of Applied Mathematics, Dalian University of Technology, Dalian, P.R. China 116024;Institute of Systems Engineering, Dalian University of Technology, Dalian, P.R. China 116024;Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, P.R. China 100084;Department of Applied Mathematics, Dalian University of Technology, Dalian, P.R. China 116024
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

This paper addresses blind source separation problem for noisy data based on the concepts of nonlinear innovation and Gaussian moments. An objective function which incorporates Gaussian moments and the nonlinear innovation of original sources is developed. Minimizing this objective function, a noisy blind source separation algorithm is proposed when the noise covariance is known and source signals are nonstationary in the sense that the variance of each is assumed to change smoothly as a function of time. In addition, this method is further extended to the case of noise covariance unknown. Validity and performance of the described approaches are demonstrated by computer simulations.