State Space Estimation

State Space Estimation is a statistical inference method that belongs to the class of dynamic system modeling techniques, distinguished by its use of latent state variables and observed measurements to recursively estimate the evolving internal state of a system over time according to probabilistic state-space models.

1.3K

Publications

78.5K

Citations

2.8K

Authors

1K

Institutions

Publications per year

2017–2026

280

Authors

2.8K

  1. MV
    Michel Verhaegen

    Delft University of Technology, Netherlands

    18 publications · 1.9K citations

    15

    h-index

  2. FD
    Feng Ding

    Jiangnan University, China

    14 publications · 1.5K citations

    12

    h-index

  3. YG
    Ya Gu

    Jiangnan University, China

    11 publications · 528 citations

    11

    h-index

  4. TB
    Thomas B. Schön

    Linköping University, Sweden

    10 publications · 568 citations

    10

    h-index

  5. BD
    Bart De Moor

    KU Leuven, Belgium

    9 publications · 1.1K citations

    9

    h-index

1–5 of 2.8K

Venues

  1. Automatica

    44 publications · 3.8K citations

    31

    h-index

  2. IEEE Transactions on Automatic Control

    55 publications · 4.9K citations

    31

    h-index

  3. IEEE Transactions on Power Systems

    32 publications · 3.6K citations

    27

    h-index

  4. IEEE Transactions on Signal Processing

    29 publications · 2.6K citations

    25

    h-index

  5. arXiv (Cornell University)

    Repository · 24 publications · 811 citations

    16

    h-index

1–5