Kalman Filter Localization is a probabilistic state estimation technique within the broader class of recursive Bayesian filters that employs the Kalman filter framework to recursively predict and update the position and orientation of a target entity by optimally fusing noisy sensor measurements with dynamic motion models under the assumption of linear system dynamics and Gaussian noise distributions.
279
Publications
11.9K
Citations
848
Authors
339
Institutions
Publications per year
2017–2026
78
Publications
279
Evaluation of global monitoring and forecasting systems at Mercator Océan
Jean‐Michel Lellouche, O. Le Galloudec, Marie Drévillon et al. · Ocean science · 2013 · 299 citations · Full text
An experimental comparison of localization methods
J.-S. Gutmann, Wolfram Burgard, D. Fox et al. · 2002 · 264 citations
1–5 of 279
Authors
848
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Institutions
339
Centre National de la Recherche Scientifique
Paris, France
10 h-index
19 publications
1.1K citations
Universidad de Zaragoza
Zaragoza, Spain
7 h-index
15 publications
1K citations
Université de Technologie de Compiègne
Compiègne, France
5 h-index
11 publications
686 citations
The University of Sydney
Sydney, Australia
5 h-index
9 publications
716 citations
Northeastern University
Shenyang, China
4 h-index
14 publications
233 citations
Heuristics and Diagnostics for Complex Systems
Compiègne, France
4 h-index
9 publications
310 citations
University of Southern California
Los Angeles, United States
4 h-index
8 publications
1.6K citations
University of Jinan
Jinan, China
3 h-index
10 publications
224 citations
1–8 of 339
Venues
- Sensors
10 publications · 269 citations
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- IEEE Access
8 publications · 434 citations
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- Journal of Intelligent & Robotic Systems
5 publications · 245 citations
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- Robotics and Autonomous Systems
4 publications · 180 citations
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- IFAC Proceedings Volumes
3 publications · 122 citations
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