Journal ArticleDOI
High-degree cubature Kalman filter
Bin Jia,Ming Xin,Yang Cheng +2 more
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TLDR
A numerical integration problem and a target tracking problem are utilized to demonstrate the necessity of using the high-degree cubature rules to improve the performance of the cubature Kalman filter.About:
This article is published in Automatica.The article was published on 2013-02-01. It has received 403 citations till now. The article focuses on the topics: Extended Kalman filter & Invariant extended Kalman filter.read more
Citations
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Journal ArticleDOI
Robust student’s t based nonlinear filter and smoother
TL;DR: Novel Student's t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions.
Journal ArticleDOI
Gaussian filters for parameter and state estimation
TL;DR: This paper presents a tutorial on the main Gaussian filters that are used for state estimation of stochastic dynamic systems and describes the main concept of state estimation based on the Bayesian paradigm and Gaussian assumption of the noise.
Journal ArticleDOI
Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon
TL;DR: A systematic introduction to the Bayesian state estimation framework is offered and various Kalman filtering U+0028 KF U-0029 techniques are reviewed, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KFFor nonlinear systems.
Journal ArticleDOI
Cooperative space object tracking using space-based optical sensors via consensus-based filters
TL;DR: Simulation results indicate that cooperative space object tracking algorithms provide better results than algorithms using a single sensor, the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space objecttracking case shown in the paper.
Journal ArticleDOI
Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise
TL;DR: It is proved that the MCUKF and MCUIF will converge to UKF and UIF, respectively, while existing MCU IF will not in this case and it generally has poor estimation accuracy as well.
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.
Book
Monte Carlo Statistical Methods
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
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
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.