Dynamic Model Fusion

Dynamic Model Fusion is a methodological framework within track-to-track fusion that integrates heterogeneous dynamic models from distinct tracking systems by adaptively aligning their state representations and temporal behaviors to achieve coherent and globally optimal track consolidation.

29

Publications

1.3K

Citations

117

Authors

46

Institutions

Publications per year

2017–2026

12

Authors

117

  1. LX
    Linfeng Xu

    Northwestern Polytechnical University, China

    2 publications · 157 citations

    2

    h-index

  2. RJ
    Ramesh Jain

    University of California, Irvine, United States

    2 publications · 61 citations

    2

    h-index

  3. ZD
    Zhansheng Duan

    Xi'an Jiaotong University, China

    2 publications · 157 citations

    2

    h-index

  4. QL
    Qinghua Lu

    Commonwealth Scientific and Industrial Research Organisation, Australia

    2 publications · 353 citations

    2

    h-index

  5. DK
    D.Y. Kuramura

    University of California San Diego, United States

    2 publications · 61 citations

    2

    h-index

1–5 of 117

Venues

  1. IEEE Transactions on Industrial Informatics

    2 publications · 92 citations

    2

    h-index

  2. Journal of Peace Research

    1 publications · 35 citations

    1

    h-index

  3. Physical Review C

    PRC · 1 publications · 14 citations

    1

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 1 publications · 22 citations

    1

    h-index

  5. IEEE Control Systems Letters

    1 publications · 28 citations

    1

    h-index

1–5