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The Iterated Kalman Smoother as a Gauss–Newton Method
Abstract
The Kalman smoother is known to be the maximum likelihood estimator when the measurement and transition functions are affine; i.e., a linear function plus a constant. A new proof of this result is presented that shows that the Kalman smoother decomposes a large least squares problem into a sequence of much smaller problems. The iterated Kalman smoother is then presented and shown to be a Gauss–Newton method for maximizing the likelihood function in the nonaffine case. This method takes advantage of the decomposition obtained with the Kalman smoother.
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Cited by
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Metadata
- Authors
- Bell, B. M.
- Venue
- SIAM Journal on Optimization
- Published
- 1994-08
- Volume
- 4
- Issue
- 3
- Pages
- 626-636
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W2091077439
- Cited by
- 163
- References
- 0
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Iterated Kalman Smoother as a Gauss–Newton Method. https://app.cochat.ai/papers/10.1137/0804035
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- Bell, B. M. (1994). The Iterated Kalman Smoother as a Gauss–Newton Method. SIAM Journal on Optimization. https://doi.org/10.1137/0804035
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