Bayesian State Estimation

Bayesian State Estimation is a statistical framework for state estimation that uses Bayesian inference to recursively update the probability distribution of a system's state by combining prior knowledge with observed evidence, distinguishing itself from other estimation methods through its principled incorporation of uncertainty via Bayes' theorem.

183

Publications

11.8K

Citations

509

Authors

240

Institutions

Publications per year

2017–2026

61

Authors

509

  1. SZ
    Shunyi Zhao

    Jiangnan University, China

    6 publications · 288 citations

    6

    h-index

  2. BH
    Biao Huang

    University of Alberta, Canada

    5 publications · 354 citations

    5

    h-index

  3. LT
    Lang Tong

    Cornell University, United States

    4 publications · 251 citations

    4

    h-index

  4. UD
    Uwe D. Hanebeck

    Karlsruhe Institute of Technology, Germany

    4 publications · 79 citations

    4

    h-index

  5. KR
    Kursat Rasim Mestav

    Cornell University, United States

    4 publications · 251 citations

    4

    h-index

1–5 of 509

Venues

  1. IEEE Transactions on Automatic Control

    9 publications · 2.2K citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 206 citations

    7

    h-index

  3. IEEE Transactions on Signal Processing

    7 publications · 2.3K citations

    7

    h-index

  4. IEEE Transactions on Power Systems

    4 publications · 368 citations

    4

    h-index

  5. Mechanical Systems and Signal Processing

    MSSP · 4 publications · 139 citations

    4

    h-index

1–5