Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/453
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Type: Journal article
Title: On-line almost-sure parameter estimation for partially observed discrete-time linear systems with known noise characteristics
Author: Elliott, R.
Ford, J.
Moore, J.
Citation: International Journal of Adaptive Control and Signal Processing, 2002; 16(6):435-453
Publisher: John Wiley & Sons Ltd
Issue Date: 2002
ISSN: 0890-6327
1099-1115
Statement of
Responsibility: 
Robert J. Elliott, Jason J. Ford, John B. Moore
Abstract: <jats:title>Abstract</jats:title><jats:p>In this paper we discuss parameter estimators for fully and partially observed discrete‐time linear stochastic systems (in state‐space form) with known noise characteristics. We propose finite‐dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost‐sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley &amp; Sons, Ltd.</jats:p>
Keywords: adaptive estimation
parameter estimation
system identification
Description: The definitive version may be found at www.wiley.com
DOI: 10.1002/acs.703
Published version: http://www3.interscience.wiley.com/cgi-bin/abstract/91013642/ABSTRACT
Appears in Collections:Applied Mathematics publications
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