The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The scaled unscented transformation
Abstract
This paper describes a generalisation of the unscented transformation (UT) which allows sigma points to be scaled to an arbitrary dimension. The UT is a method for predicting means and covariances in nonlinear systems. A set of samples are deterministically chosen which match the mean and covariance of a (not necessarily Gaussian-distributed) probability distribution. These samples can be scaled by an arbitrary constant. The method guarantees that the mean and covariance second order accuracy in mean and covariance, giving the same performance as a second order truncated filter but without the need to calculate any Jacobians or Hessians. The impacts of scaling issues are illustrated by considering conversions from polar to Cartesian coordinates with large angular uncertainties.
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Citations
Cites
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Stochastic Processes and Filtering Theory
(1970) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(09)x6022-4 · Cited by 7,302
New extension of the Kalman filter to nonlinear systems
Julier, S. J., & Uhlmann, J. K. (1997) · SPIE Proceedings · doi:10.1117/12.280797 · Cited by 5,331
Self-citationA 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
Self-citationStochastic models, estimation, and control
(1982) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(08)x6186-7 · Cited by 3,229
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
Self-citationTracking with debiased consistent converted measurements versus EKF
Lerro, D., & Bar-Shalom, Y. (1993) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.220948 · Cited by 483
Consistent debiased method for converting between polar and Cartesian coordinate systems
Julier, S., & Uhlmann, J. K. (1997) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.277178 · Cited by 151
Self-citationSimultaneous localisation and map building using split covariance intersection
Julier, S. J., & Uhlmann, J. K. (2002) · doi:10.1109/iros.2001.977155 · Cited by 59
Self-citation
Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models
van der Merwe, R. (2004) · doi:10.6083/m4z60kz5 · Cited by 1,017
Creating Fragility Functions for Performance‐Based Earthquake Engineering
Porter, K., Kennedy, R., & Bachman, R. (2007) · Earthquake Spectra · doi:10.1193/1.2720892 · Cited by 685
Simultaneous Localization, Mapping and Moving Object Tracking
Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., & Durrant-Whyte, H. (2007) · The International Journal of Robotics Research · doi:10.1177/0278364907081229 · Cited by 596
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
Kelly, J., & Sukhatme, G. S. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364910382802 · Cited by 536
Metadata
- Authors
- Julier, S. J.
- Venue
- Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
- Publisher
- IEEE
- Published
- 2002
- Pages
- 4555-4559 vol.6
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W2107802551
- Cited by
- 1,114
- References
- 9
- FWCI
- 16.31
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
