Nonlinear Filtering
Nonlinear Filtering is a filtering methodology that estimates the state of a nonlinear dynamical system from noisy observations by recursively updating the posterior probability distribution of the state, distinguishing itself from linear filtering techniques through its ability to handle nonlinearity in both the system dynamics and the observation model.
2.4K
Publications
158.6K
Citations
4.3K
Authors
1.4K
Institutions
Publications per year
2017–2026
459
2.4K
Unscented Filtering and Nonlinear Estimation
Simon Julier, Jeffrey Uhlmann · Proceedings of the IEEE · 2004 · 6.5K citations
Computing, Data Science, Forecasting +14
The unscented Kalman filter for nonlinear estimation
Eric A. Wan, Rudolph van der Merwe · 2002 · 3.9K citations
Ienkaran Arasaratnam, S. Haykin · IEEE Transactions on Automatic Control · 2009 · 3.3K citations
Computing, Computational Engineering, Bayesian Data Analysis +11
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1.4K

Brunel University of London
London, United Kingdom
34 h-index
62 publications
5.9K citations

Centre National de la Recherche Scientifique
Paris, France
28 h-index
99 publications
5.4K citations

Harbin Institute of Technology
Harbin, China
26 h-index
93 publications
6.3K citations
Massachusetts Institute of Technology
Cambridge, United States
23 h-index
62 publications
5.8K citations

Northwestern Polytechnical University
Xi'an, China
22 h-index
80 publications
4.3K citations

University of Trieste
Trieste, Italy
22 h-index
39 publications
1.4K citations

Harbin Engineering University
Harbin, China
20 h-index
83 publications
3.2K citations
New York University
New York, United States
20 h-index
38 publications
3K citations
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118 publications · 20K citations
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97 publications · 8.4K citations
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48 publications · 5K citations
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54 publications · 2.5K citations
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42 publications · 2.7K citations
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