DocumentCode :
795385
Title :
A simple geometric blind source separation method for bounded magnitude sources
Author :
Erdogan, Alper T.
Author_Institution :
Dept. of Electr. & Electron. Eng., Koc Univ., Istanbul, Turkey
Volume :
54
Issue :
2
fYear :
2006
Firstpage :
438
Lastpage :
449
Abstract :
A novel blind source separation approach and the corresponding adaptive algorithm is presented. It is assumed that the observation mixture is obtained through an unknown memoryless linear mapping of independent and bounded magnitude sources. We further assume an initial adaptive prewhitening of the original observation vector which transforms it into a white vector with the same dimension as the original source vector. Our approach is centered around the basic geometric fact that, under a certain boundedness assumption, the unitary mapping which transforms the whitening output vector into an independent vector has the minimum value of maximum (real component) magnitude output over the ensemble of all output components. Therefore, the related criterion is the minimization of the infinity norm of the real component of the unitary separator´s output over all possible output combinations. For the minimization of the corresponding nondifferentiable cost function, we propose the use of subgradient optimization methods to obtain a low complexity iterative adaptive solution. The resulting algorithm is fairly intuitive and simple, and provides a low complexity solution especially to a class of multiuser digital communications problems. We provide examples at the end of this paper to illustrate the performance of our algorithm.
Keywords :
adaptive signal processing; blind source separation; geometric programming; gradient methods; bounded magnitude sources; geometric blind source separation method; iterative adaptive algorithm; memoryless linear mapping; subgradient optimization methods; whitening vector; Adaptive algorithm; Blind source separation; Cost function; Digital communication; H infinity control; Iterative algorithms; Iterative methods; Minimization methods; Optimization methods; Vectors; Adaptive filtering; blind source separation; independent component analysis; induced matrix norm; kurtosis; multiple-input multiple-output (MIMO) blind equalization; subgradient;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2005.861800
Filename :
1576974
Link To Document :
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