Interacting Multiple Model Filtering
Interacting Multiple Model Filtering is a sequential state estimation framework that belongs to the class of multiple model filtering methods, distinguished by its incorporation of probabilistic interactions between parallel Kalman filters through model transition probabilities in a Markov chain to adaptively estimate system dynamics in the presence of abrupt and uncertain changes.
281
Publications
15K
Citations
659
Authors
289
Institutions
Publications per year
2017–2026
76
Variants
Imm AlgorithmImm FilteringInteracting Multiple Model Algorithm
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University of Connecticut
Storrs, United States
13 h-index
34 publications
3.7K citations

McMaster University
Hamilton, Canada
12 h-index
26 publications
1.1K citations

Beihang University
Beijing, China
10 h-index
22 publications
608 citations

Naval Surface Warfare Center
Washington, United States
10 h-index
21 publications
860 citations
Netherlands Aerospace Centre
Amsterdam, Netherlands
7 h-index
9 publications
3.1K citations
University of Sussex
Brighton, United Kingdom
6 h-index
12 publications
544 citations

United States Air Force Research Laboratory
Wright-Patterson AFB, United States
6 h-index
8 publications
251 citations
Auburn University
Auburn, United States
6 h-index
10 publications
343 citations
1–8 of 289
37 publications · 2.2K citations
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16 publications · 327 citations
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9 publications · 276 citations
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7 publications · 194 citations
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6 publications · 250 citations
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