Statistical Signal Processing

Statistical Signal Processing is a branch of signal processing that employs statistical methods to analyze, model, and infer information from signals in the presence of uncertainty, distinguishing itself through the formal incorporation of probabilistic reasoning to characterize and exploit signal and noise properties.

681

Publications

74.4K

Citations

1.6K

Authors

646

Institutions

Publications per year

2017–2026

107

Authors

1.6K

  1. AS
    Alan S. Willsky

    Massachusetts Institute of Technology, United States

    7 publications · 591 citations

    7

    h-index

  2. JC
    José C. Prı́ncipe

    University of Florida, United States

    6 publications · 346 citations

    6

    h-index

  3. AO
    Alfred O. Hero

    University of Michigan, United States

    6 publications · 3.2K citations

    6

    h-index

  4. VK
    Vikram Krishnamurthy

    University of British Columbia, Canada

    6 publications · 314 citations

    6

    h-index

  5. DM
    David Middleton

    Loughborough University, United Kingdom

    5 publications · 536 citations

    5

    h-index

1–5 of 1.6K

Venues

  1. IEEE Transactions on Signal Processing

    41 publications · 4.7K citations

    25

    h-index

  2. IEEE Signal Processing Magazine

    29 publications · 6.5K citations

    24

    h-index

  3. IEEE Transactions on Information Theory

    23 publications · 1.9K citations

    20

    h-index

  4. IEEE Signal Processing Letters

    10 publications · 518 citations

    10

    h-index

  5. Technometrics

    10 publications · 15.8K citations

    10

    h-index

1–5