Nonlinear Filtering

Nonlinear Filtering is a filtering methodology that estimates the state of a nonlinear dynamical system from noisy observations by recursively updating the posterior probability distribution of the state, distinguishing itself from linear filtering techniques through its ability to handle nonlinearity in both the system dynamics and the observation model.

2.4K

Publications

158.6K

Citations

4.3K

Authors

1.4K

Institutions

Publications per year

2017–2026

459

Authors

4.3K

  1. ZW
    Zidong Wang

    Brunel University of London, United Kingdom

    46 publications · 4.2K citations

    35

    h-index

  2. PS
    Peng Shi

    The University of Adelaide, Australia

    23 publications · 1.7K citations

    19

    h-index

  3. GL
    Giovanni L. Sicuranza

    University of Trieste, Italy

    23 publications · 859 citations

    18

    h-index

  4. SS
    Stephen S.‐T. Yau

    University of Illinois Chicago, United States

    23 publications · 722 citations

    16

    h-index

  5. AC
    Alberto Carini

    University of Trieste, Italy

    19 publications · 667 citations

    15

    h-index

1–5 of 4.3K

Venues

  1. IEEE Transactions on Automatic Control

    118 publications · 20K citations

    54

    h-index

  2. IEEE Transactions on Signal Processing

    97 publications · 8.4K citations

    48

    h-index

  3. Automatica

    48 publications · 5K citations

    34

    h-index

  4. Signal Processing

    54 publications · 2.5K citations

    30

    h-index

  5. 27

    h-index

1–5