The DOI matches its Crossref record.
A non-divergent estimation algorithm in the presence of unknown correlations
Abstract
This paper addresses the problem of estimation when the cross-correlation in the errors between different random variables are unknown. A new data fusion algorithm, the covariance intersection algorithm (CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman filter.
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Cited by
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Bahr, A., & Leonard, J. J. (2008) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-540-77457-0_36 · Cited by 266
DDF-SAM 2.0: Consistent distributed smoothing and mapping
Cunningham, A., Indelman, V., & Dellaert, F. (2013) · 2013 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2013.6631323 · Cited by 171
Metadata
- Authors
- Julier, S. J., & Uhlmann, J. K.
- Venue
- Proceedings of the 1997 American Control Conference (Cat. No.97CH36041)
- Publisher
- IEEE
- Published
- 1997
- Pages
- 2369-2373 vol.4
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W2148234182
- Cited by
- 999
- References
- 0
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A non-divergent estimation algorithm in the presence of unknown correlations. https://app.cochat.ai/papers/10.1109/acc.1997.609105
- The paper
- Julier, S. J., & Uhlmann, J. K. (1997). A non-divergent estimation algorithm in the presence of unknown correlations. Proceedings of the 1997 American Control Conference (Cat. No.97CH36041). https://doi.org/10.1109/acc.1997.609105
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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.
