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A new blind deconvolution algorithm based on the probability distribution method
Author(s) -
Matsumoto Hiroki,
Furukura Toshihiro
Publication year - 2007
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20317
Subject(s) - kurtosis , deconvolution , blind deconvolution , sample (material) , algorithm , convergence (economics) , reliability (semiconductor) , computer science , function (biology) , mathematics , statistics , power (physics) , chemistry , physics , chromatography , evolutionary biology , economics , biology , economic growth , quantum mechanics
Blind deconvolution is a method of recovering transmitted signals from only received signals. The probability distribution method is one of the blind deconvolution methods. This method has two problems: it has slower convergence and its reliability is lower. In this paper, we propose a new algorithm for solving these two problems. The proposed algorithm is as follows. (1) It is based on the adaptive processing with each sample. (2) Kurtosis is adaptively estimated by recovered signals with each sample. (3) Cost function is decided by kurtosis. (4) Transmitted signals are recovered by received signals using decided cost function on the sample time. We confirm the validity of the new algorithm by computer simulation. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 56–65, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20317