Bayesian Sensor Fusion
Bayesian Sensor Fusion is a probabilistic integration framework within sensor fusion that employs Bayesian inference to recursively update the belief about a system's state by combining prior knowledge with new sensor observations, distinguishing itself through its rigorous treatment of uncertainty and sequential data assimilation.
774
Publications
38.8K
Citations
2.1K
Authors
750
Institutions
Publications per year
2017–2026
222
774
Multi-sensor optimal information fusion Kalman filter
Shuli Sun, Zili Deng · Automatica · 2004 · 825 citations
Bayesian filtering for location estimation
D. Fox, Jeffrey Hightower, Lin Liao et al. · IEEE Pervasive Computing · 2003 · 706 citations
1–5 of 774
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Centre National de la Recherche Scientifique
Paris, France
19 h-index
61 publications
5.1K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
13 h-index
33 publications
3.6K citations
University of Illinois Urbana-Champaign
Urbana, United States
12 h-index
21 publications
1.3K citations
RMIT University
Melbourne, Australia
12 h-index
31 publications
1.5K citations

The University of Melbourne
Melbourne, Australia
11 h-index
23 publications
2.3K citations

The University of Sydney
Sydney, Australia
11 h-index
31 publications
1.8K citations

Northwestern Polytechnical University
Xi'an, China
11 h-index
27 publications
842 citations

University of Electronic Science and Technology of China
Chengdu, China
10 h-index
34 publications
2K citations
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28 publications · 3.6K citations
23
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22 publications · 1.7K citations
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18 publications · 1.4K citations
18
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16 publications · 850 citations
16
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27 publications · 552 citations
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