Decision-level Fusion

Decision-Level Fusion is a type of sensor fusion in which the highest-level processed data—individual decisions derived from separate sensor modalities or sources—are combined to produce a single, more reliable or accurate decision, distinguished from data-level or feature-level fusion by operating on abstracted, symbolic outputs rather than raw or intermediate sensor data.

3.3K

Publications

184.2K

Citations

8.3K

Authors

2.2K

Institutions

Publications per year

2017–2026

1.2K

Authors

8.3K

  1. PK
    Pramod K. Varshney

    Syracuse University, United States

    47 publications · 4K citations

    28

    h-index

  2. EB
    Erik Blasch

    United States Air Force Research Laboratory, United States

    39 publications · 1.9K citations

    24

    h-index

  3. YD
    Yong Deng

    University of Electronic Science and Technology of China, China

    26 publications · 1.7K citations

    20

    h-index

  4. JD
    Jean Dezert

    Office National d'Études et de Recherches Aérospatiales, France

    23 publications · 2.3K citations

    19

    h-index

  5. DC
    Domenico Ciuonzo

    University of Naples Federico II, Italy

    17 publications · 1.2K citations

    17

    h-index

1–5 of 8.3K

Venues

  1. Information Fusion

    217 publications · 23.8K citations

    77

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  2. 34

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  3. Sensors

    64 publications · 2.6K citations

    31

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  4. IEEE Transactions on Signal Processing

    42 publications · 3.6K citations

    31

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  5. IEEE Access

    51 publications · 2.9K citations

    27

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1–5