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

Authors

659

  1. YB
    Yaakov Bar‐Shalom

    University of Connecticut, United States

    13 publications · 3.3K citations

    12

    h-index

  2. YJ
    Yingmin Jia

    Beihang University, China

    8 publications · 243 citations

    8

    h-index

  3. WB
    W.D. Blair

    Naval Surface Warfare Center, United States

    8 publications · 351 citations

    8

    h-index

  4. GA
    Gregory A. Watson

    Naval Surface Warfare Center, United States

    7 publications · 171 citations

    7

    h-index

  5. WL
    Wenling Li

    Beihang University, China

    7 publications · 195 citations

    7

    h-index

1–5 of 659

Venues

  1. 24

    h-index

  2. 12

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  3. Sensors

    9 publications · 276 citations

    9

    h-index

  4. Journal of Guidance Control and Dynamics

    7 publications · 194 citations

    7

    h-index

  5. IEEE Transactions on Vehicular Technology

    6 publications · 250 citations

    6

    h-index

1–5