Decision-level Sensor Fusion

Decision-Level Sensor Fusion is a type of sensor fusion process in which the highest-level information—individual decisions from separate sensors or modalities—is combined to produce a more reliable, accurate, or robust collective decision, distinguished from data-level or feature-level fusion by operating on abstracted, local inference outcomes rather than raw or intermediate data.

274

Publications

15.3K

Citations

812

Authors

347

Institutions

Publications per year

2017–2026

110

Authors

812

  1. PK
    Pramod K. Varshney

    Syracuse University, United States

    12 publications · 2.5K citations

    12

    h-index

  2. DC
    Domenico Ciuonzo

    University of Naples Federico II, Italy

    7 publications · 452 citations

    7

    h-index

  3. PS
    Pierluigi Salvo Rossi

    Norwegian University of Science and Technology, Norway

    6 publications · 414 citations

    6

    h-index

  4. RN
    Ruixin Niu

    Syracuse University, United States

    6 publications · 697 citations

    6

    h-index

  5. RJ
    Ruixiang Jiang

    Syracuse University, United States

    4 publications · 669 citations

    4

    h-index

1–5 of 812

Venues

  1. Sensors

    15 publications · 631 citations

    13

    h-index

  2. IEEE Sensors Journal

    10 publications · 568 citations

    10

    h-index

  3. IEEE Transactions on Signal Processing

    7 publications · 1K citations

    7

    h-index

  4. 6

    h-index

  5. IEEE Access

    5 publications · 159 citations

    5

    h-index

1–5