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FREQUENCY OVERLAPPED SIGNAL IDENTIFICATION USING BLIND SOURCE SEPARATION
被引：5
作者：
WANG Junfeng College of Mechanical Science and Engineering
机构：
基金：
中国国家自然科学基金;
关键词：
Principal component analysis(PCA) Independent component analysis(ICA) Blind source separation (BSS);
D O I：
暂无
中图分类号：
TN911.2 [信息论];
学科分类号：
070104 ;
081101 ;
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.
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页码：286 / 289
页数：4
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