Kalman Filtering

Kalman Filtering is a state estimation algorithm that belongs to the class of recursive Bayesian filters and specifically employs a weighted least-squares criterion to optimally estimate the state of a linear dynamic system in the presence of Gaussian noise.

18.2K

Publications

1M

Citations

31.9K

Authors

4.7K

Institutions

Publications per year

2017–2026

5.4K

Authors

31.9K

  1. ZW
    Zidong Wang

    Brunel University of London, United Kingdom

    113 publications · 8.4K citations

    49

    h-index

  2. YB
    Yaakov Bar‐Shalom

    University of Connecticut, United States

    80 publications · 11.4K citations

    37

    h-index

  3. YZ
    Yonggang Zhang

    Harbin Engineering University, China

    62 publications · 5K citations

    36

    h-index

  4. YH
    Yulong Huang

    Harbin Engineering University, China

    49 publications · 4.5K citations

    35

    h-index

  5. SS
    Shuli Sun

    Heilongjiang University, China

    72 publications · 4.3K citations

    33

    h-index

1–5 of 31.9K

Venues

  1. IEEE Transactions on Automatic Control

    579 publications · 72.1K citations

    126

    h-index

  2. IEEE Transactions on Signal Processing

    347 publications · 44.1K citations

    95

    h-index

  3. 92

    h-index

  4. Automatica

    310 publications · 32.1K citations

    92

    h-index

  5. Monthly Weather Review

    176 publications · 29.3K citations

    72

    h-index

1–5