Journal ArticleDOI
Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty
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TLDR
In this paper, a modified strong tracking unscented Kalman filter (MSTUKF) was proposed to address the performance degradation and divergence of the unscenting Kalman filters because of process model uncertainty.Abstract:
This paper presents a modified strong tracking unscented Kalman filter MSTUKF to address the performance degradation and divergence of the unscented Kalman filter because of process model uncertainty. The MSTUKF adopts the hypothesis testing method to identify process model uncertainty and further introduces a defined suboptimal fading factor into the prediction covariance to decrease the weight of the prior knowledge on filtering solution. The MSTUKF not only corrects the state estimation in the occurrence of process model uncertainty but also avoids the loss of precision for the state estimation in the absence of process model uncertainty. Further, it does not require the cumbersome evaluation of Jacobian matrix involved in the calculation of the suboptimal fading factor. Experimental results and comparison analysis demonstrate the effectiveness of the proposed MSTUKF. Copyright © 2015John Wiley & Sons, Ltd.read more
Citations
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Journal ArticleDOI
Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system
TL;DR: A new adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty for vehicular INS/GPS integration.
Journal ArticleDOI
Noise covariance matrices in state-space models: A survey and comparison of estimation methods—Part I
TL;DR: This paper deals with the estimation of the noise covariance matrices of systems described by state‐space models and a simulation comparison using exemplary MATLAB implementations of the methods is provided.
Journal ArticleDOI
Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter
TL;DR: A novel adaptive UKF is presented by combining the maximum likelihood principle (MLP) and moving horizon estimation (MHE) to overcome this limitation of the classical unscented Kalman filter.
Journal ArticleDOI
A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty
Yanwen Li,Chao Wang,Jinfeng Gong +2 more
TL;DR: In this paper, a recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly, and the statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty.
Journal ArticleDOI
Interacting multiple model estimation-based adaptive robust unscented Kalman filter
TL;DR: In this article, an interacting multiple model (IMMIMM) estimation-based adaptive robust unscented Kalman filter (UKF) is proposed to estimate the state of nonlinear dynamic systems.
References
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Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Journal ArticleDOI
Unscented filtering and nonlinear estimation
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Proceedings ArticleDOI
The unscented Kalman filter for nonlinear estimation
Eric A. Wan,R. van der Merwe +1 more
TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.