Proceedings ArticleDOI
Convergence analysis of the extended Kalman filter as an observer for nonlinear discrete-time systems
Mohamed Boutayeb,Hugues Rafaralahy,Mohamed Darouach +2 more
- Vol. 2, pp 1555-1560
TLDR
A modified version of the extended Kalman filter (EKF), when used as an observer for nonlinear discrete-time systems, is presented, and efficiency of this approach is shown through a nonlinear identification problem.Abstract:
In the current paper, a modified version of the extended Kalman filter (EKF), when used as an observer for nonlinear discrete-time systems, is presented. The approach proposed consists in introducing time varying matrices to enhance convergence and stability of the obtained estimator. Based on a new formulation of the first order linearization technique, sufficient conditions to ensure local asymptotic convergence are established. Efficiency of this approach, compared to the classical version of the EKF, is shown through a nonlinear identification problem.read more
Citations
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Journal ArticleDOI
Stochastic stability of the discrete-time extended Kalman filter
TL;DR: It is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough.
Proceedings ArticleDOI
The over-extended Kalman filter - don't use it!
D.F. Bizup,D.E. Brown +1 more
TL;DR: In this paper, an alternative linearization of range rate that is not a function of the position elements is derived, which can improve a tracking system's velocity estimates without risk to its position estimates.
Journal ArticleDOI
Joint Estimation of Battery Parameters and State of Charge Using an Extended Kalman Filter: A Single-Parameter Tuning Approach
TL;DR: This article combines a nonlinear observer with the structured representation of model uncertainty and disturbances as typically used in a robust-observer design approach and presents the joint EKF for simultaneous estimation of SoC and model parameters.
Journal ArticleDOI
Adaptive generalized generic model control and stability analysis
TL;DR: An adaptive control method—adaptive generalized generic model control (AGGMC) is proposed for a class of nonlinear time-varying processes by use of a modified strong tracking filter (MSTF) and is proved to be Lyapunov stable.
Dissertation
Adaptative high-gain extended Kalman filter and applications
TL;DR: The Adaptive High-Gain EKF is proposed, a solution to the "observability problem" for nonlinear dynamic systems that takes advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors, and the parameter that is the heart of the high-gain technique is made adaptive.
References
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Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Journal ArticleDOI
Linearization by output injection and nonlinear observers
Arthur J. Krener,Alberto Isidori +1 more
TL;DR: Observers can easily be constructed for those nonlinear systems which can be transformed into a linear system by change of state variables and output injection.
Journal ArticleDOI
Nonlinear observers with linearizable error dynamics
TL;DR: In this article, a method for designing asymptotic observers for a class of nonlinear systems is presented, where the error between the state of the systems and the observer in appropriate coordinates evolves linearly and can be made to decay aribtrarily exponentially fast.
Journal ArticleDOI
Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems
TL;DR: In this paper, a convergence analysis of the extended Kalman filter for nonlinear systems with unknown parameters is given, and it is shown that in general the estimates may be biased or divergent and the causes for this are displayed.
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