Adaptive Filtering

Adaptive Filtering is a signal processing technique that belongs to the class of dynamic estimation methods, distinguished by its ability to iteratively adjust filter coefficients in real time to minimize the error between a desired signal and the filter output, thereby effectively suppressing radio frequency interference in acoustic and other signal domains.

5K

Publications

259.4K

Citations

8.8K

Authors

2.2K

Institutions

Publications per year

2017–2026

838

Authors

8.8K

  1. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    47 publications · 4.2K citations

    29

    h-index

  2. NB
    N.J. Bershad

    University of California, Irvine, United States

    42 publications · 2.3K citations

    28

    h-index

  3. BC
    Badong Chen

    Xi'an Jiaotong University, China

    44 publications · 3.1K citations

    27

    h-index

  4. JB
    Jacob Benesty

    Institut National de la Recherche Scientifique, Canada

    47 publications · 2.2K citations

    27

    h-index

  5. HZ
    Haiquan Zhao

    Southwest Jiaotong University, China

    41 publications · 1.8K citations

    25

    h-index

1–5 of 8.8K

Venues

  1. IEEE Transactions on Signal Processing

    499 publications · 31.9K citations

    94

    h-index

  2. 65

    h-index

  3. IEEE Signal Processing Letters

    143 publications · 8.9K citations

    48

    h-index

  4. Signal Processing

    167 publications · 5.6K citations

    41

    h-index

  5. IEEE Transactions on Circuits and Systems

    64 publications · 4.1K citations

    38

    h-index

1–5