Topic
Invariant extended Kalman filter
About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.
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TL;DR: The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements and shows that the error on the state estimate falls below 1% in less than 200 s despite a 30% error on battery initial state-of-charge and additive measurement noise.
189 citations
TL;DR: In this paper, a recursive equation for the actual covariance matrix of the estimation error when the filter design is based upon erroneous models is derived, and the derived equation can also be used to obtain the covariance matrices when the optimal filter gains are approximated by simple functions of time to be used in real-time filtering application.
Abstract: The optimal filtering equations, as derived by Kalman [1], [2], require the specification of a number of models for a given application. This paper concerns itself with the effect of errors in the assumed models on the filter response. The types of errors considered are those in the covariance of the initial state vector, the covariance of the stochastic inputs to the system, and the covariance of the uncorrelated measurement noise. Presented here is a derivation of a recursive equation for the actual covariance matrix of the estimation error when the filter design is based upon erroneous models. The derived equation can also be used to obtain the covariance matrix of the estimation error when the optimal filter gains are approximated by simple functions of time to be used in a real-time filtering application. A numerical example illustrates the use of the derived equations.
189 citations
Proceedings Article•
01 Aug 2007
TL;DR: In this article, the feasibility of applying Kalman filtering techniques to include dynamic state variables in the state estimation process is investigated, and the proposed Kalman filter based dynamic state estimation is tested on a multi-machine system with both large and small disturbances.
Abstract: The lack of dynamic information in the operation of power systems can be attributed to the use of steady state estimators, which generate the input values for many operational tools. This paper investigates the feasibility of applying Kalman Filtering techniques to include dynamic state variables in the state estimation process. The proposed Kalman Filter based dynamic state estimation is tested on a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance with respect to sampling rate and noise level are presented as well. The study results show that there is a promising path forward for the implementation of Kalman Filter based dynamic state estimation in conjunction with the emerging phasor measurement technologies.
188 citations
TL;DR: In this article, the application of stochastic state estimators in vehicle dynamics control is discussed, where it is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured.
Abstract: This paper deals with the application of stochastic state estimators in vehicle dynamics control. It is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured. System states that cannot be measured directly, can be estimated by a Kalman Filter. The idea of the Kalman filter is to implement a model of the real system in an on-board computer in parallel with the system itself. This paper will give 3 examples of this principle applied to automotive systems.
187 citations
TL;DR: Experimental results illustrate that the proposed adaptive extended Kalman filter has better localization accuracy than existing state-of-the-art algorithms.
Abstract: To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.
184 citations