Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency

In this correspondence, we consider the problem of multi-input multi output (MIMO) quasi maximum likelihood (QML) blind deconvolution. We examine two classes of estimators, which are commonly believed to be suitable for super and sub-Gaussian sources. We state the consistency conditions and demonstr...

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Published in:IEEE transactions on signal processing 2005-07, Vol.53 (7), p.2576-2579
Main Authors: Bronstein, A.M., Bronstein, M.M., Zibulevsky, M.
Format: Article
Language:English
Subjects:
Blind deconvolution
Blind source separation
Blinds
Consistency
Deconvolution
Delay estimation
Estimators
Image restoration
kurtosis
Maximum likelihood estimation
MIMO
Probability density function
quasi-maximum likelihood
Sensor systems
Signal processing
Source separation
State estimation
sub-Gaussian
super-Gaussian
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cited_by cdi_FETCH-LOGICAL-c351t-5096229597024646600df5e198f37f901b74f2e59cff63f9577f8027c4ba24403
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description In this correspondence, we consider the problem of multi-input multi output (MIMO) quasi maximum likelihood (QML) blind deconvolution. We examine two classes of estimators, which are commonly believed to be suitable for super and sub-Gaussian sources. We state the consistency conditions and demonstrate a source distribution, for which the studied estimators are unsuitable, in the sense that they are inconsistent.
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source IEEE Electronic Library (IEL) Journals
subjects Blind deconvolution
Blind source separation
Blinds
Consistency
Deconvolution
Delay estimation
Estimators
Image restoration
kurtosis
Maximum likelihood estimation
MIMO
Probability density function
quasi-maximum likelihood
Sensor systems
Signal processing
Source separation
State estimation
sub-Gaussian
super-Gaussian
title Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency
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