Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm
CHEN Hong-Bin1, FENG Jiu-Chao1, FANG Yong2
1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 5106412School of Communication and Information Engineering, Shanghai University, Shanghai 200072
Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm
CHEN Hong-Bin1;FENG Jiu-Chao1;FANG Yong2
1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 5106412School of Communication and Information Engineering, Shanghai University, Shanghai 200072
摘要We report the results of using the fast independent component analysis (FastICA) algorithm to realize blind extraction of chaotic signals. Two cases are taken into consideration: namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail. It is also shown that regarding a noise as an independent source is not always correct.
Abstract：We report the results of using the fast independent component analysis (FastICA) algorithm to realize blind extraction of chaotic signals. Two cases are taken into consideration: namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail. It is also shown that regarding a noise as an independent source is not always correct.
(Telecommunications: signal transmission and processing; communication satellites)
引用本文:
CHEN Hong-Bin;FENG Jiu-Chao;FANG Yong. Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm[J]. 中国物理快报, 2008, 25(2): 405-408.
CHEN Hong-Bin, FENG Jiu-Chao, FANG Yong. Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm. Chin. Phys. Lett., 2008, 25(2): 405-408.
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2008, Vol. 25 
