NDLI: Blind Identification Of Independent Components With Higher-order Statistics

Content Provider	IEEE Xplore Digital Library
Author	Cardoso, J.-F.
Copyright Year	1989
Abstract	We consider a N-variate random process consisting in N (or less) independent components corrupted by additive noise. Identifying (or separating) these components without any a-priori information about their structure (blind identification) is theorically possible under the independence assumption. We propose a blind algorithm based on second- and fourth-order statistics. Its main feature is the use of tensor formalism allowing blind identification to be performed as a generalized eigen-decomposition of the "quadricovariance tensor". All non-Gaussian components (and possibly one Gaussian component) are separated even if they have identical probability distributions. Simulations in Array Processing context are included.
Starting Page	157
Ending Page	162
File Size	419390
Page Count	6
File Format	PDF
DOI	10.1109/HOSA.1989.735288
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-06-28
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additive noise Tensile stress Array signal processing Covariance matrix Random processes Signal resolution Network address translation Higher order statistics Principal component analysis Sensor arrays
Content Type	Text
Resource Type	Article

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