The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Understanding the Kalman Filter
Abstract
This is an expository article. Here we show how the successfully used Kalman filter, popular with control engineers and other scientists, can be easily understood by statisticians if we use a Bayesian formulation and some well-known results in multivariate statistics. We also give a simple example illustrating the use of the Kalman filter for quality control work.
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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
Cited by
Rational Learning and Partisan Attitudes
Gerber, A., & Green, D. P. (1998) · American Journal of Political Science · doi:10.2307/2991730 · Cited by 295
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv024 · Cited by 128
Bayesian estimation and the Kalman filter
Barker, A. L., Brown, D. E., & Martin, W. N. (1995) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(95)00156-s · Cited by 78
Chen, Y., Cao, F., Cheng, W., Liu, B., & Yu, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-42791-x · Cited by 6
Metadata
- Authors
- Meinhold, R. J., & Singpurwalla, N. D.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1983-05
- Volume
- 37
- Issue
- 2
- Pages
- 123-127
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2075628905
- Cited by
- 644
- References
- 1
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Understanding the Kalman Filter. https://app.cochat.ai/papers/10.1080/00031305.1983.10482723
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- Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman Filter. The American Statistician. https://doi.org/10.1080/00031305.1983.10482723
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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.
