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Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems

Ljung, L.·1979·IEEE Transactions on Automatic Control

Abstract

The extended Kalman filter is an approximate filter for nonlinear systems, based on first-order linearization. Its use for the joint parameter and state estimation problem for linear systems with unknown parameters is well known and widely spread. Here a convergence analysis of this method is given. It is shown that in general, the estimates may be biased or divergent and the causes for this are displayed. Some common special cases where convergence is guaranteed are also given. The analysis gives insight into the convergence mechanisms and it is shown that with a modification of the algorithm, global convergence results can be obtained for a general case. The scheme can then be interpreted as maximization of the likelihood function for the estimation problem, or as a recursive prediction error algorithm.

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Metadata

Authors
Ljung, L.
Venue
IEEE Transactions on Automatic Control
Published
1979-02
Volume
24
Issue
1
Pages
36-50
ISSN
0018-9286 (ISSN-L)
OpenAlex
W1981364618
Cited by
1,162
References
10
FWCI
18.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. https://app.cochat.ai/papers/10.1109/tac.1979.1101943
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Ljung, L. (1979). Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1979.1101943

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