Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/933576
- Title
- A scenario-based approach to parameter estimation in state-space models having quantized output data
- Author/Creator
-
Marelli, Damián E.;
Godoy, Boris I.;
Goodwin, Graham C.
- Institution
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering
- Description
- In this paper we describe an algorithm for estimating the parameters of a linear, discrete-time system, in state-space form, having quantized measurements. The estimation is carried out using the maximum likelihood criterion. The solution is found using the expectation maximization (EM) algorithm. A technical difficulty in applying this algorithm for this problem is that the a posteriori probability density function, found in the EM algorithm, is non-Gaussian. To deal with this issue, we sequentially approximate it using scenarios, i.e., a weighted sum of impulses which are deterministically computed. Numerical experiments show that the proposed approach leads to a significantly more accurate estimation than the one obtained by ignoring the presence of the quantizer and applying standard estimation methods.
- Relation
- 49th IEEE Conference on Decision and Control (CDC 2010). Proceedings of the 49th IEEE Conference on Decision and Control (Atlanta, GA 15-17 December, 2010) p. 2011-2016
- Publisher Link
- http://dx.doi.org/10.1109/CDC.2010.5717022
- Date
- 2010
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Keyword(s)
-
parameters;
EM algorithm;
state-space models
- Resource Type
- conference paper
- Rights
- Copyright © 2010 IEEE. Reprinted from the Proceedings of the 49th IEEE Conference on Decision and Control. 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/1959.13/933576
- Identifier
- ISBN:9781424477449
- Identifier
- ISSN:0743-1546
- Reviewed
-
-
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