Particle Filters
Particle Filter is a sequential Monte Carlo method in data assimilation that represents the Bayesian posterior distribution of a system's state using a set of random samples, or particles, propagated through time according to the dynamics of the system and weighted by their observational likelihood, thereby enabling nonlinear, non-Gaussian state estimation in ocean data assimilation and related fields.
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Smc Methods
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Filtering via Simulation: Auxiliary Particle Filters
M. Pitt, Neil Shephard · Journal of the American Statistical Association · 1999 · 2.3K citations · Full text
Computing, Physics, Statistics +13
Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas et al. · 2000 · 1.4K citations
Petar M. Djurić, J.H. Kotecha, J. Zhang et al. · IEEE Signal Processing Magazine · 2003 · 953 citations
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University of Cambridge
Cambridge, United Kingdom
32 h-index
100 publications
32.5K citations
Stony Brook University
Stony Brook, United States
29 h-index
100 publications
9.9K citations

Linköping University
Linköping, Sweden
29 h-index
102 publications
15.7K citations

Centre National de la Recherche Scientifique
Paris, France
26 h-index
72 publications
4.6K citations
University of Maryland, College Park
College Park, United States
18 h-index
44 publications
2K citations
Georgia Institute of Technology
Atlanta, United States
18 h-index
60 publications
5.8K citations
RTX (United States)
Arlington, United States
18 h-index
59 publications
3.1K citations
University of California, Berkeley
Berkeley, United States
17 h-index
44 publications
5.1K citations
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