Blind deconvolution via cumulant extrema

Classical deconvolution is concerned with the task of recovering an excitation signal, given the response of a known time-invariant linear operator to that excitation. Deconvolution is discussed along with its more challenging counterpart, blind deconvolution, where no knowledge of the linear operat...

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Published in:IEEE signal processing magazine 1996-05, Vol.13 (3), p.24-42
Main Author: Cadzow, J.A.
Format: Magazinearticle
Language:English
Subjects:
Clouds
Data mining
Deconvolution
Indexing
Probability density function
Random variables
Signal design
Signal processing
Temperature
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description Classical deconvolution is concerned with the task of recovering an excitation signal, given the response of a known time-invariant linear operator to that excitation. Deconvolution is discussed along with its more challenging counterpart, blind deconvolution, where no knowledge of the linear operator is assumed. This discussion focuses on a class of deconvolution algorithms based on higher-order statistics, and more particularly, cumulants. These algorithms offer the potential of superior performance in both the noise free and noisy data cases relative to that achieved by other deconvolution techniques. This article provides a tutorial description as well as presenting new results on many of the fundamental higher-order concepts used in deconvolution, with the emphasis on maximizing the deconvolved signal's normalized cumulant.
doi_str_mv 10.1109/79.489267
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subjects Clouds
Data mining
Deconvolution
Indexing
Probability density function
Random variables
Signal design
Signal processing
Temperature
title Blind deconvolution via cumulant extrema
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