The DOI matches its Crossref record.
A new method for the nonlinear transformation of means and covariances in filters and estimators
Abstract
This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parametrize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
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-citationCubature 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
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
The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods
Jin, X.-B., Robert Jeremiah, R. J., Su, T.-L., Bai, Y.-T., & Kong, J.-L. (2021) · Sensors · doi:10.3390/s21062085 · Cited by 137
Metadata
- Authors
- Julier, S., Uhlmann, J., & Durrant-Whyte, H. F.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2000-03
- Volume
- 45
- Issue
- 3
- Pages
- 477-482
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2121990344
- Cited by
- 3,735
- References
- 0
- FWCI
- 24.99
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A new method for the nonlinear transformation of means and covariances in filters and estimators. https://app.cochat.ai/papers/10.1109/9.847726
- The paper
- Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.847726
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
