Parallel Particle Filtering

Parallel Particle Filtering is a computational framework within the class of particle filtering methods that employs concurrent processing of multiple particles or particle sets across distributed or multi-core architectures to enhance estimation accuracy and reduce computational latency in nonlinear, non-Gaussian state-space models.

64

Publications

2.4K

Citations

162

Authors

92

Institutions

Publications per year

2017–2026

9

Authors

162

  1. PM
    Petar M. Djurić

    Stony Brook University, United States

    6 publications · 519 citations

    6

    h-index

  2. MB
    Miodrag Bolić

    University of Ottawa, Canada

    5 publications · 500 citations

    5

    h-index

  3. SH
    Sangjin Hong

    Stony Brook University, United States

    3 publications · 146 citations

    3

    h-index

  4. JJ
    Jun Jason Zhang

    University of Denver, United States

    3 publications · 76 citations

    3

    h-index

  5. JP
    Johnny Park

    Purdue University West Lafayette, United States

    3 publications · 79 citations

    3

    h-index

1–5 of 162

Venues

  1. IEEE Transactions on Signal Processing

    5 publications · 435 citations

    5

    h-index

  2. Electromagnetic waves

    PIER · 2 publications · 37 citations

    2

    h-index

  3. 2

    h-index

  4. Journal of Signal Processing Systems

    2 publications · 36 citations

    2

    h-index

  5. Digital Signal Processing

    1 publications · 140 citations

    1

    h-index

1–5