DocumentCode :
2685268
Title :
Canonical decomposition of even higher order cumulant arrays for blind underdetermined mixture identification
Author :
Karfoul, Ahmad ; Albera, Laurent ; De Lathauwer, Lieven
Author_Institution :
INSERM, Rennes
fYear :
2008
fDate :
21-23 July 2008
Firstpage :
501
Lastpage :
505
Abstract :
A new family of methods, named 2q-ORBIT (q > 1), is proposed in this paper in order to blindly identify potentially underdetermined mixtures of statistically independent sources. These methods are based on the canonical decomposition of q-th order (q ges 2) cumulants. The latter decomposition is brought back to the decomposition of a third order array whose one loading matrix is unitary. Such a decomposition is then computed by alterning and repeating two schemes until convergence: the first one consists in solving a Procrustes problem while the second one needs to compute the best rank-1 approximation of several q-th order arrays. Computer results show a good efficiency of the proposed methods with respect to classical cumulant-based algorithms especially in the underdetermined case.
Keywords :
array signal processing; convergence; higher order statistics; singular value decomposition; Procrustes problem; blind underdetermined mixture identification; canonical decomposition; convergence; higher order cumulant arrays; loading matrix; singular value decomposition; Array signal processing; Biosensors; Capacitive sensors; Convergence; Iterative algorithms; Iterative methods; Least squares approximation; Least squares methods; Matrix decomposition; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensor Array and Multichannel Signal Processing Workshop, 2008. SAM 2008. 5th IEEE
Conference_Location :
Darmstadt
Print_ISBN :
978-1-4244-2240-1
Electronic_ISBN :
978-1-4244-2241-8
Type :
conf
DOI :
10.1109/SAM.2008.4606921
Filename :
4606921
Link To Document :
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