Particle Filter Estimation
Particle Filter Estimation is a sequential Bayesian filtering method that uses a set of random samples, or particles, to approximate the posterior distribution of a system's state, distinguishing itself from other estimation techniques by its ability to handle non-linear and non-Gaussian state-space models through recursive Monte Carlo sampling.
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Publications
19.4K
Citations
1.4K
Authors
573
Institutions
Publications per year
2017–2026
129
Variants
Sequential Monte Carlo EstimationSmc Estimation
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573

Centre National de la Recherche Scientifique
Paris, France
11 h-index
23 publications
1.3K citations

Linköping University
Linköping, Sweden
8 h-index
20 publications
941 citations

University of Waterloo
Waterloo, Canada
8 h-index
17 publications
672 citations
University of Cambridge
Cambridge, United Kingdom
7 h-index
11 publications
780 citations

Harbin Institute of Technology
Harbin, China
7 h-index
26 publications
882 citations

The University of Melbourne
Melbourne, Australia
7 h-index
7 publications
352 citations
The University of Tokyo
Tokyo, Japan
6 h-index
21 publications
781 citations

University of British Columbia
Vancouver, Canada
6 h-index
7 publications
226 citations
1–8 of 573
Repository · 10 publications · 207 citations
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10 publications · 453 citations
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9 publications · 809 citations
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7 publications · 497 citations
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7 publications · 342 citations
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