Blind signal separation of linear mixture using trilinear decomposition

2009 ◽  
Vol 26 (5) ◽  
pp. 608-613 ◽  
Author(s):  
Xiaofei Zhang ◽  
Dazhuan Xu
Keyword(s):  
Signal Separation ◽  
Blind Signal Separation ◽  
Trilinear Decomposition ◽  
Blind Signal

A Note on Stone's Conjecture of Blind Signal Separation

2005 ◽  
Vol 17 (2) ◽  
pp. 321-330 ◽  
Author(s):  
Shengli Xie ◽  
Zhaoshui He ◽  
Yuli Fu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
The Novel ◽  
Blind Signal ◽  
Simple Simulation ◽  
Novel Approaches

Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

A Method for Extracting Signals with Specific Normalized Kurtosis Range

2013 ◽  
Vol 756-759 ◽  
pp. 3845-3848
Author(s):  
Yong Jian Zhao ◽  
Mei Xia Qu ◽  
Hai Ning Jiang
Keyword(s):  
A Priori ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Validity And Reliability ◽  
Original Source ◽  
Blind Source Extraction ◽  
Blind Signal ◽  
Extraction Algorithm ◽  
Fastica Algorithm ◽  
Source Extraction

The famous FastICA algorithm has been widely used for blind signal separation. For every process, it only converges to an original source which has the maximum negentropy of the underlying signals. To ensure the first output is the desired signal, we incorporate a priori knowledge as a constraint into the FastICA algorithm to construct a robust blind source extraction algorithm. One can extract the desired signal if its normalized kurtosis is known to lie in a specific range, whereas other unwanted signals do not belong to this range. Experimental results on biomedical signals illustrate the validity and reliability of the proposed method.

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