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.

479

Publications

19.4K

Citations

1.4K

Authors

573

Institutions

Publications per year

2017–2026

129

Variants

Sequential Monte Carlo EstimationSmc Estimation

Authors

1.4K

  1. AD
    Arnaud Doucet

    University of Cambridge, United Kingdom

    6 publications · 458 citations

    6

    h-index

  2. LM
    Lyudmila Mihaylova

    University of Sheffield, United Kingdom

    6 publications · 437 citations

    6

    h-index

  3. BR
    Branko Ristić

    Defence Science and Technology Group, Australia

    5 publications · 255 citations

    5

    h-index

  4. PM
    Petar M. Djurić

    Stony Brook University, United States

    5 publications · 170 citations

    5

    h-index

  5. TB
    Thomas B. Schön

    Linköping University, Sweden

    4 publications · 221 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. SSRN Electronic Journal

    Repository · 10 publications · 207 citations

    10

    h-index

  2. Sensors

    10 publications · 453 citations

    10

    h-index

  3. IEEE Transactions on Signal Processing

    9 publications · 809 citations

    9

    h-index

  4. Journal of Process Control

    7 publications · 497 citations

    7

    h-index

  5. 7

    h-index

1–5