Decision-level Sensor Fusion
Decision-Level Sensor Fusion is a type of sensor fusion process in which the highest-level information—individual decisions from separate sensors or modalities—is combined to produce a more reliable, accurate, or robust collective decision, distinguished from data-level or feature-level fusion by operating on abstracted, local inference outcomes rather than raw or intermediate data.
274
Publications
15.3K
Citations
812
Authors
347
Institutions
Publications per year
2017–2026
110
Parents
274
1–5 of 274
812
12
h-index
7
h-index
Norwegian University of Science and Technology, Norway
6 publications · 414 citations
6
h-index
6
h-index
4
h-index
1–5 of 812
347
Syracuse University
Syracuse, United States
14 h-index
32 publications
6.3K citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
16 publications
1.4K citations

University of Campania "Luigi Vanvitelli"
Caserta, Italy
5 h-index
13 publications
1K citations
Dynetics (United States)
Huntsville, United States
5 h-index
5 publications
600 citations

Northwestern Polytechnical University
Xi'an, China
5 h-index
16 publications
482 citations

University of Naples Federico II
Naples, Italy
5 h-index
10 publications
492 citations
Tsinghua University
Beijing, China
4 h-index
13 publications
721 citations
The University of Texas at Dallas
Richardson, United States
4 h-index
8 publications
860 citations
1–8 of 347
15 publications · 631 citations
13
h-index
10 publications · 568 citations
10
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7 publications · 1K citations
7
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6 publications · 1.4K citations
6
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5 publications · 159 citations
5
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1–5