Probabilistic Sensor Fusion
Probabilistic Sensor Fusion is a formal framework for integrating data from multiple sensors by modeling uncertainty and dependencies through probability theory, distinguishing itself from deterministic fusion methods through its explicit representation and manipulation of probabilistic beliefs.
308
Publications
15.3K
Citations
772
Authors
348
Institutions
Publications per year
2017–2026
63
Parents
308
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
Furqan Alam, Rashid Mehmood, Iyad Katib et al. · IEEE Access · 2017 · 385 citations · Full text
Entropy-based sensor selection heuristic for target localization
Hanbiao Wang, Kung Yao, Greg Pottie et al. · 2004 · 314 citations · Full text
1–5 of 308
772
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1–5 of 772
348
University of Illinois Urbana-Champaign
Urbana, United States
9 h-index
21 publications
1.2K citations
Lockheed Martin (United States)
Bethesda, United States
8 h-index
9 publications
341 citations

Centre National de la Recherche Scientifique
Paris, France
8 h-index
21 publications
1.2K citations
Syracuse University
Syracuse, United States
8 h-index
18 publications
665 citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
15 publications
1.3K citations
RMIT University
Melbourne, Australia
6 h-index
13 publications
557 citations

The University of Sydney
Sydney, Australia
6 h-index
16 publications
1.5K citations
Universität Ulm
Ulm, Germany
6 h-index
22 publications
1.6K citations
1–8 of 348
13 publications · 293 citations
11
h-index
10 publications · 2.1K citations
10
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8 publications · 479 citations
8
h-index
7 publications · 335 citations
7
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5 publications · 373 citations
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