Blind Source Separation Algorithm Based on Wavelet Smoothing for Super-Gaussian and Sub-Gaussian Signals
BI Xue1,2, CHEN Xiang-dong2, WANG Shi-hai2, ZHANG Yu3
1. Network Center, Xihua University, Chengdu 610039, China; 2. School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China; 3. Institute of Drill Technology, Sichuan Petroleum Management Bureau, Guanghan 618300, China
Abstract：In order to separate super-Gaussian and sub-Gaussian signals,this paper uses high-and low-frequency coefficients of wavelet transform as smooth factors,then builds a signal-to-noise ratio objective function,which uses the denominator as prediction error and can be optimized to resolve separable matrix.Simulation shows that this algorithm can separate source signals effectively.
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BI Xue, CHEN Xiang-dong, WANG Shi-hai, ZHANG Yu. Blind Source Separation Algorithm Based on Wavelet Smoothing for Super-Gaussian and Sub-Gaussian Signals. Information and control, 2007, 36(6): 696-701.
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