PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Blind Identification of Underdetermined Mixtures based on the Hexacovariance
Laurent Albera, Pierre Comon, Pascal Chevalier and Anne Ferreol
In: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, 2004, Montreal, Canada.

Abstract

Static linear mixtures with more sources than sensors are considered. Blind Identification (BI) of underdetermined mixtures is addressed by taking advantage of Sixth Order (SixO) statistics and the Virtual Array (VA) concept. Surprisingly, identification methods solely based on the hexacovariance well succeed, despite their expected high estimation variance; this is due to the inherently good conditioning of the problem. A computationally simple but efficient algorithm, named BIRTH, is proposed and enables to identify the steering vectors of up to P=N^2-N+1 sources for arrays of N sensors with space diversity only, and up to P=N^2 for those with angular and polarization diversities. Five numerical algorithms are compared.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:137
Deposited By:Pierre Comon
Deposited On:31 May 2004