The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation
Abstract
A probabilistic framework, called Sigma-point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter (EKF). SPKF methods are superior to the standard EKF based estimation approaches, as an SPKF achieves second-order or higher accuracy. The SPKF has also been applied to the integrated navigation problem as it relates to unmanned aerial vehicle (UAV) autonomy. The SPKF-based sensor latency compensation technique is used to demonstrate the lagged GPS measurements.
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
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Haykin, S. (1986) · Cited by 12,603
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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
Self-citationGELB, A. (1974) · CERN Document Server (European Organization for Nuclear Research) · Cited by 6,405
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-citationOscillation and Chaos in Physiological Control Systems
Mackey, M. C., & Glass, L. (1977) · Science · doi:10.1126/science.267326 · Cited by 4,135
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
Self-citation
Cited by
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
Metadata
- Authors
- van der Merwe, R., Wan, E., & Julier, S.
- Venue
- AIAA Guidance, Navigation, and Control Conference and Exhibit
- Published
- 2004-06-22
- OpenAlex
- W1539436111
- Cited by
- 465
- References
- 29
- FWCI
- 24.32
- Type
- conference paper
- 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: Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. https://app.cochat.ai/papers/10.2514/6.2004-5120
- The paper
- van der Merwe, R., Wan, E., & Julier, S. (2004). Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. AIAA Guidance, Navigation, and Control Conference and Exhibit. https://doi.org/10.2514/6.2004-5120
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.
