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.

1.7K

Publications

105.7K

Citations

3.4K

Authors

1.1K

Institutions

Publications per year

2017–2026

319

Authors

3.4K

  1. PM
    Petar M. Djurić

    Stony Brook University, United States

    47 publications · 5.9K citations

    27

    h-index

  2. FG
    Fredrik Gustafsson

    Linköping University, Sweden

    30 publications · 4.6K citations

    23

    h-index

  3. AD
    Arnaud Doucet

    University of Cambridge, United Kingdom

    30 publications · 5.2K citations

    20

    h-index

  4. FD
    Fred Daum

    RTX (United States), United States

    29 publications · 1.8K citations

    19

    h-index

  5. JH
    Jim Huang

    RTX (United States), United States

    26 publications · 856 citations

    18

    h-index

1–5 of 3.4K

Venues

  1. IEEE Transactions on Signal Processing

    67 publications · 21K citations

    36

    h-index

  2. 19

    h-index

  3. Monthly Weather Review

    24 publications · 3K citations

    18

    h-index

  4. 18

    h-index

  5. arXiv (Cornell University)

    Repository · 27 publications · 715 citations

    16

    h-index

1–5