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Maximum-likelihood estimation of delta-domain model parameters from noisy output signals

Kadirkamanathan, V. and Anderson, S.R. (2008) Maximum-likelihood estimation of delta-domain model parameters from noisy output signals. IEEE Transactions on Signal Processing, 56 (8 (par). pp. 3765-3770. ISSN 1053-587X


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Fast sampling is desirable to describe signal transmission through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Delta operator, EM algorithm, expectation-maximization, fast sampling, system identification
Institution: The University of Sheffield
Academic Units: The University of Sheffield > University of Sheffield Research Centres and Institutes > Centre for Signal Processing in NeuroImaging and Systems Neuroscience (Sheffield)
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Ms Suzannah Rockett
Date Deposited: 28 Aug 2008 17:40
Last Modified: 08 Feb 2013 16:56
Published Version: http://dx.doi.org/10.1109/TSP.2008.920443
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: 10.1109/TSP.2008.920443
URI: http://eprints.whiterose.ac.uk/id/eprint/4462

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