- Title
- Prefilter design for errors in variables model identification
- Creator
- Mahata, Kaushik
- Relation
- 45th IEEE Conference on Decision and Control. Proceedings of the 45th IEEE Conference on Decision and Control (San Diego, CA 2006) p. 175-180
- Publisher Link
- http://dx.doi.org/10.1109/CDC.2006.377269
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2006
- Description
- The bias compensated least squares approach for errors-in-variables model identification is examined in a new framework, where it is allowed to prefilter the observed input-output data prior to the estimation process. A statistical analysis of the estimation algorithm is presented. Subsequently, it is shown how these prefilters and the weighting matrix can be tuned in order to optimize the estimation accuracy. According to the numerical simulation results, the covariance matrix of the estimated parameter vector is very close to the Cramer-Rao lower bound for the estimation problem.
- Subject
- covariance matrices; estimation theory; least squares approximations; parameter estimation; statistical analysis; vectors
- Identifier
- http://hdl.handle.net/1959.13/30031
- Identifier
- uon:2650
- Identifier
- ISBN:1424401712
- Rights
- Copyright © 2006 IEEE. Reprinted from Proceedings of the 45th IEEE Conference on Decision and Control, 175-180. 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.
- Language
- eng
- Full Text
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