A novel blind deconvolution method via maximum entropy

Monika Pinchas, Ben Zion Bobrovsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a new closed form (approximated) expression for the conditional expectation that is based on Maximum Entropy. This expression does not rely on the knowledge of the convolutional noise power nor imposes any restrictions on the probability distribution of the unobserved input sequence and is suitable for the general case of real and complex source signals. In addition, we propose a set of algebraic linear equations for the Lagrange multipliers related to the blind deconvolution problem that can be easily computed in a non iterative approach. Our new derivation leads to a new blind deconvolution algorithm with improved equalization performance compared with Godard's equalizer.

Original languageEnglish
Title of host publication2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PublisherIEEE Computer Society
Pages351-356
Number of pages6
ISBN (Print)0780394046, 9780780394049
DOIs
StatePublished - 2005
Event2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France
Duration: 17 Jul 200520 Jul 2005

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2005

Conference

Conference2005 IEEE/SP 13th Workshop on Statistical Signal Processing
Country/TerritoryFrance
CityBordeaux
Period17/07/0520/07/05

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