The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The optimum linear smoother as a combination of two optimum linear filters
Abstract
A solution to the optimum linear smoothing problem is presented in which the smoother is interpreted as a combination of two optimum linear filters. This result is obtained from the well-known equation for the maximum likelihood combination of two independent estimates and equivalence to previous formulations is demonstrated. Forms of the solution which are convenient for practical computation are developed.
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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
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Li, X. R., & Jilkov, V. P. (2005) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2005.1561886 · Cited by 1,157
Recursive state estimation for a set-membership description of uncertainty
Bertsekas, D., & Rhodes, I. (1971) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1971.1099674 · Cited by 719
A Survey of Sequential Monte Carlo Methods for Economics and Finance
Creal, D. (2012) · Econometric Reviews · doi:10.1080/07474938.2011.607333 · Cited by 301
Havlicek, M., Jan, J., Brazdil, M., & Calhoun, V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.063 · Cited by 106
Metadata
- Authors
- Fraser, D., & Potter, J.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1969-08
- Volume
- 14
- Issue
- 4
- Pages
- 387-390
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2161129698
- Cited by
- 376
- References
- 1
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fraser, D., & Potter, J. (1969). The optimum linear smoother as a combination of two optimum linear filters. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1969.1099196
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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.
