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The interacting multiple model algorithm for systems with Markovian switching coefficients
Abstract
An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients.>
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Cited by
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Khodarahmi, M., & Maihami, V. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09815-7 · Cited by 561
A perception‐driven autonomous urban vehicle
Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20262 · Cited by 429
Li, S., Fang, H., & Shi, B. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107542 · Cited by 190
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Metadata
- Authors
- Blom, H. A. P., & Bar-Shalom, Y.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1988
- Volume
- 33
- Issue
- 8
- Pages
- 780-783
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2117397690
- Cited by
- 2,460
- References
- 0
- FWCI
- 10.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Blom, H. A. P., & Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.1299
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