The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Some Relations Between Extended and Unscented Kalman Filters
Open accessAbstract
The unscented Kalman filter (UKF) has become a popular alternative to the extended Kalman filter (EKF) during the last decade. UKF propagates the so called sigma points by function evaluations using the unscented transformation (UT), and this is at first glance very different from the standard EKF algorithm which is based on a linearized model. The claimed advantages with UKF are that it propagates the first two moments of the posterior distribution and that it does not require gradients of the system model. We point out several less known links between EKF and UKF in terms of two conceptually different implementations of the Kalman filter: the standard one based on the discrete Riccati equation, and one based on a formula on conditional expectations that does not involve an explicit Riccati equation. First, it is shown that the sigma point function evaluations can be used in the classical EKF rather than an explicitly linearized model. Second, a less cited version of the EKF based on a second-order Taylor expansion is shown to be quite closely related to UKF. The different algorithms and results are illustrated with examples inspired by core observation models in target tracking and sensor network applications.
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Cites
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
Julier, S. J., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,588
The unscented Kalman filter for nonlinear estimation
Wan, E. A., & Van Der Merwe, R. (2002) · Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373) · doi:10.1109/asspcc.2000.882463 · Cited by 4,001
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
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
Stochastic models, estimation, and control
(1982) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(08)x6186-7 · Cited by 3,229
Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software
Bar‐Shalom, Y., Kirubarajan, T., & Li, X. R. (2001) · Cited by 2,175
A new approach for filtering nonlinear systems
Julier, S. J., Uhlmann, J. K., & Durrant-Whyte, H. F. (2005) · Proceedings of 1995 American Control Conference - ACC'95 · doi:10.1109/acc.1995.529783 · Cited by 1,926
Particle filters for positioning, navigation, and tracking
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., & Nordlund, P.-J. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978396 · Cited by 1,701
Self-citationGustafsson, F., & Gunnarsson, F. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1458284 · Cited by 996
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A 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
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
A Systematization of the Unscented Kalman Filter Theory
Menegaz, H. M. T., Ishihara, J. Y., Borges, G. A., & Vargas, A. N. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2404511 · Cited by 278
Metadata
- Authors
- Gustafsson, F., & Hendeby, G.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2012-02
- Volume
- 60
- Issue
- 2
- Pages
- 545-555
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2049650637
- Cited by
- 390
- References
- 16
- FWCI
- 16.85
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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