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FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION 被引量:6

FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION
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摘要 The concepts, principles and usages of principal component analysis (PCA) and independent component analysis (ICA) are interpreted. Then the algorithm and methodology of ICA-based blind source separation (BSS), in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not. The concepts, principles and usages of principal component analysis (PCA) and independent component analysis (ICA) are interpreted. Then the algorithm and methodology of ICA-based blind source separation (BSS), in which the pre-whitened based on PCA for observed signals is used, are researched. Aiming at the mixture signals, whose frequency components are overlapped by each other, a simulation of BSS to separate this type of mixture signals by using theory and approach of BSS has been done. The result shows that the BSS has some advantages what the traditional methodology of frequency analysis has not.
出处 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第2期286-289,共4页 中国机械工程学报(英文版)
基金 This project is supported by National Natural Science Foundation of China(No.50405033).
关键词 Principal component analysis(PCA) Independent component analysis(ICA) Blind source separation (BSS) Principal component analysis(PCA) Independent component analysis(ICA) Blind source separation (BSS)
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  • 1KARHUNEN J,WANG Liuyue,VIGARIO R.Nonlinear PCA type approaches for source separation and independent component analysis[C]//IEEE Proceedings of the 1995 IEEE International Conference on Neural Networks,1995,Perth,Aust.,Piscataway,NJ,USA,1995,2:995-1000.
  • 2GESBERT D,PAPADIAS C B,PAULRAJ A.Blind equalization of polyphase FIR channels:whitening approach[C]//IEEE Comp.Soc.Proceedings of the 1997 31st Asilomar Conference on Signals,Systems &Computers,1997,Pacific Grove,CA,USA,1997,2:1604-1608.
  • 3OJA E.Principal components,minor components,and linear neural networks[J].Neural Networks,1992,5:927-935.
  • 4LOCHER L,SAISAN P.Blind source separation using independent component Analysis[R].EE214-Final Project,Department of Electrical Engineering,University of California,Los Angeles,2001:3-15.
  • 5COMON P.Independent component analysis-a new concept?[J].Signal Processing,1994,36:287-314.
  • 6KARHUNEN J,OJA E,WANG L,et al.A class of neural networks for independent component analysis[J].Neural Networks,IEEE Transactions on,1997,8 (3):486-504.

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