The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Cubature Kalman Filters
Abstract
In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named thecubatureKalmanfilter(CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-degree spherical-radial cubature rule that provides a set of cubature points scaling linearly with the state-vector dimension. The CKF may therefore provide a systematic solution for high-dimensional nonlinear filtering problems. The paper also includes the derivation of a square-root version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the second-order statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
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Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
Bar‐Shalom, Y., Li, X. ‐., & Kirubarajan, T. (2002) · doi:10.1002/0471221279 · Cited by 6,782
Unscented Filtering and Nonlinear Estimation
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A new method for the nonlinear transformation of means and covariances in filters and estimators
Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/9.847726 · Cited by 3,735
Adaptive Control Processes: A Guided Tour
Bellman, R. E. (1961) · doi:10.1515/9781400874668 · Cited by 2,802
Monte Carlo Strategies in Scientific Computing
Liu, J. S. (2004) · Springer Series in Statistics · doi:10.1007/978-0-387-76371-2 · Cited by 2,690
Kalman Filtering: Theory and Practice Using Matlab®
S. Grewal, M., & P. Andrews, A. (2014) · doi:10.1002/9781118984987 · Cited by 2,117
Introduction to Numerical Analysis
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Gaussian filters for nonlinear filtering problems
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Cited by
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations
Arasaratnam, I., Haykin, S., & Hurd, T. R. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2056923 · Cited by 580
Self-citationA Review on Kalman Filter Models
Khodarahmi, M., & Maihami, V. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09815-7 · Cited by 561
Applied Stochastic Differential Equations
Särkkä, S., & Solin, A. (2019) · doi:10.1017/9781108186735 · Cited by 326
Research and Development of Electro-hydraulic Control Valves Oriented to Industry 4.0: A Review
Xu, B., Shen, J., Liu, S., Su, Q., & Zhang, J. (2020) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-020-00446-2 · Cited by 193
Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles
Espedal, I. B., Jinasena, A., Burheim, O. S., & Lamb, J. J. (2021) · Energies · doi:10.3390/en14113284 · Cited by 177
Metadata
- Authors
- Arasaratnam, I., & Haykin, S.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2009-06
- Volume
- 54
- Issue
- 6
- Pages
- 1254-1269
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2054091988
- Cited by
- 3,380
- References
- 37
- FWCI
- 70.84
- Type
- journal article
- Language
- en
- Open access
- closed
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