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WANG Junfeng
College of Mechanical Science
and Engineering,
Huazhong University of Science
and Technology,
Wuhan 430074, China
SHI Tielin
Wuhan National Laboratory for
Optoelectronics,
Wuhan 430074, China
HE Lingsong
YANG Shuzi
College of Mechanical Science
and Engineering,
Huazhong University of Science
and Technology,
Wuhan 430074, China
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FREQUENCY
OVERLAPPED SIGNAL
IDENTIFICATION USING BLIND
SOURCE SEPARATION*
Abstract:
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.
Key words:
Principal component analysis (PCA) Independent component analysis (ICA)
Key words: Blind source separation (BSS)
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