The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Unscented Filtering and Nonlinear Estimation
Abstract
The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.
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Citations
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
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
The Gaussian Mixture Probability Hypothesis Density Filter
Vo, B.-N., & Ma, W.-K. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881190 · Cited by 1,919
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
Vo, B.-T., Vo, B.-N., & Cantoni, A. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.894241 · Cited by 824
Metadata
- Authors
- Julier, S. J., & Uhlmann, J. K.
- Venue
- Proceedings of the IEEE
- Published
- 2004-03
- Volume
- 92
- Issue
- 3
- Pages
- 401-422
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2123487311
- Cited by
- 6,588
- References
- 3
- FWCI
- 131.16
- Type
- journal article
- Language
- en
- Open access
- closed
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