Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/916281
- Title
- Maximum likelihood estimation of state space models from frequency domain data
- Author/Creator
-
Wills, Adrian;
Ninness, Brett;
Gibson, Stuart
- Institution
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
- Description
- This paper addresses the problem of estimating linear time invariant models from observed frequency domain data. Here an emphasis is placed on deriving numerically robust and efficient methods that can reliably deal with high order models over wide bandwidths. This involves a novel application of the expectation-maximization algorithm in order to find maximum likelihood estimates of state space structures. An empirical study using both simulated and real measurement data is presented to illustrate the efficacy of the solutions derived here.
- Relation
- IEEE Transactions on Automatic Control Vol. 54, Issue 1, p. 19-33
- Publisher Link
- http://dx.doi.org/10.1109/TAC.2008.2009485
- Date
- 2009
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Keyword(s)
-
expectation maximization;
EM;
maximum likelihood;
ML
- Resource Type
- journal article
- Rights
- Copyright © 2009 IEEE. Reprinted from IEEE Transactions on Automatic Control Vol. 54, Issue 1, p. 19-33. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or 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 must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Identifier
- http://hdl.handle.net/1959.13/916281
- Identifier
- ISSN:0018-9286
- Reviewed
- Full Text
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