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
Parents
29
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Weishan Zhang, Tao Zhou, Qinghua Lu et al. · IEEE Internet of Things Journal · 2021 · 325 citations · Full text
Augmented virtual environments (AVE): dynamic fusion of imagery and 3D models
Ulrich Neumann, Suya You, Jinhui Hu et al. · 2003 · 72 citations
1–5 of 29
117
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Commonwealth Scientific and Industrial Research Organisation, Australia
2 publications · 353 citations
2
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2
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1–5 of 117
46

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
2 h-index
3 publications
678 citations
University of Southern California
Los Angeles, United States
2 h-index
7 publications
416 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
4 publications
339 citations
China University of Petroleum, East China
Qingdao, China
2 h-index
9 publications
1.4K citations
Tongji University
Shanghai, China
2 h-index
8 publications
502 citations
University of New Orleans
New Orleans, United States
2 h-index
2 publications
157 citations
National Ilan University
Yilan, Taiwan
1 h-index
1 publications
40 citations
Shandong University
Jinan, China
1 h-index
5 publications
260 citations
1–8 of 46
2 publications · 92 citations
2
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1 publications · 35 citations
1
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PRC · 1 publications · 14 citations
1
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MSSP · 1 publications · 22 citations
1
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1 publications · 28 citations
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1–5