Covariance Estimation

Covariance Estimation is a statistical inference method that, within the framework of Kalman Filtering and parameter estimation, characterizes the uncertainty of state or parameter estimates by specifying the covariance matrix that quantifies the expected dispersion and correlation of estimation errors around their mean values.

1.6K

Publications

139.6K

Citations

3.3K

Authors

1.3K

Institutions

Publications per year

2017–2026

335

Authors

3.3K

  1. PS
    Petre Stoica

    Uppsala University, Sweden

    18 publications · 2.4K citations

    15

    h-index

  2. AW
    Ami Wiesel

    Hebrew University of Jerusalem, Israel

    14 publications · 1.5K citations

    14

    h-index

  3. AD
    Antonio De Maio

    University of Naples Federico II, Italy

    12 publications · 777 citations

    12

    h-index

  4. SN
    Seiichi Nakamori

    Kagoshima University, Japan

    13 publications · 319 citations

    11

    h-index

  5. AH
    A. Hermoso‐Carazo

    Universidad de Granada, Spain

    13 publications · 320 citations

    10

    h-index

1–5 of 3.3K

Venues

  1. IEEE Transactions on Signal Processing

    89 publications · 7.7K citations

    47

    h-index

  2. The Annals of Statistics

    47 publications · 6.6K citations

    37

    h-index

  3. Journal of the American Statistical Association

    40 publications · 4.5K citations

    26

    h-index

  4. IEEE Transactions on Automatic Control

    42 publications · 7.4K citations

    25

    h-index

  5. 24

    h-index

1–5