Adaptive Signal Processing

Adaptive Signal Processing is a signal processing paradigm that dynamically modifies its parameters or structure in response to changes in input signal characteristics, distinguishing it from static or fixed signal processing methods through its capacity for real-time self-optimization based on ongoing performance evaluation.

2.2K

Publications

141.8K

Citations

4K

Authors

1.2K

Institutions

Publications per year

2017–2026

261

Authors

4K

  1. BW
    Bernard Widrow

    Stanford University, United States

    20 publications · 8.9K citations

    18

    h-index

  2. SD
    S.C. Douglas

    Southern Methodist University, United States

    23 publications · 940 citations

    17

    h-index

  3. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    18 publications · 813 citations

    16

    h-index

  4. NB
    N.J. Bershad

    University of California, Irvine, United States

    19 publications · 1.2K citations

    16

    h-index

  5. JC
    José C. Prı́ncipe

    University of Florida, United States

    16 publications · 1.2K citations

    14

    h-index

1–5 of 4K

Venues

  1. IEEE Transactions on Signal Processing

    265 publications · 25.4K citations

    75

    h-index

  2. 49

    h-index

  3. IEEE Signal Processing Letters

    51 publications · 3.1K citations

    30

    h-index

  4. Signal Processing

    59 publications · 3.5K citations

    26

    h-index

  5. 25

    h-index

1–5