Adaptive Particle Filtering

Adaptive Particle Filtering is a particle filtering method that dynamically adjusts its parameters, sampling strategy, or resampling mechanism in response to changes in the underlying system dynamics or observation conditions to improve estimation accuracy and computational efficiency.

302

Publications

13.8K

Citations

867

Authors

369

Institutions

Publications per year

2017–2026

92

Authors

867

  1. ZS
    Zhiguo Shi

    Zhejiang University, China

    4 publications · 123 citations

    4

    h-index

  2. JY
    Jinxia Yu

    Henan Polytechnic University, China

    4 publications · 71 citations

    4

    h-index

  3. PM
    Petar M. Djurić

    Stony Brook University, United States

    4 publications · 137 citations

    4

    h-index

  4. ME
    Marcos E. Orchard

    University of Chile, Chile

    3 publications · 271 citations

    3

    h-index

  5. ZD
    Zhuohua Duan

    Central South University, China

    3 publications · 60 citations

    3

    h-index

1–5 of 867

Venues

  1. Computer Vision and Image Understanding

    CVIU · 5 publications · 196 citations

    5

    h-index

  2. Digital Signal Processing

    5 publications · 166 citations

    5

    h-index

  3. Journal of Computational Physics

    5 publications · 308 citations

    5

    h-index

  4. Neurocomputing

    5 publications · 149 citations

    5

    h-index

  5. Signal Processing

    5 publications · 117 citations

    5

    h-index

1–5