NDLI: Maximum likelihood blind separation of convolutively mixed discrete sources

Content Provider	IEEE Xplore Digital Library
Author	Gu Fanglin Zhang Hang Zhu Desheng
Copyright Year	2013
Abstract	In this paper, a Maximum Likelihood (ML) approach, implemented by Expectation-Maximization (EM) algorithm, is proposed to blind separation of convolutively mixed discrete sources. In order to carry out the expectation procedure of the EM algorithm with a less computational load, the algorithm named Iterative Maximum Likelihood algorithm (IML) is proposed to calculate the likelihood and recover the source signals. An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter. Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources. Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures. Furthermore, the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters.
Starting Page	60
Ending Page	67
Page Count	8
File Size	636295
File Format	PDF
ISSN	16735447
Volume Number	10
Issue Number	6
Language	English
Publisher	China Communications Magazine Co. Ltd.
Publisher Date	2013-06-01
Publisher Place	China
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blind source separation Maximum likelihood estimation Sensors Source separation Filtering algorithms Algorithm design and analysis MIMO Finite Alphabet Blind Source Separation convo-lutive mixture EM
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Electrical and Electronic Engineering

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