Probabilistic Sensor Fusion

Probabilistic Sensor Fusion is a formal framework for integrating data from multiple sensors by modeling uncertainty and dependencies through probability theory, distinguishing itself from deterministic fusion methods through its explicit representation and manipulation of probabilistic beliefs.

308

Publications

15.3K

Citations

772

Authors

348

Institutions

Publications per year

2017–2026

63

Authors

772

  1. RM
    Ronald Mahler

    Lockheed Martin (United States), United States

    11 publications · 507 citations

    10

    h-index

  2. RH
    Reza Hoseinnezhad

    RMIT University, Australia

    7 publications · 340 citations

    7

    h-index

  3. PK
    Pramod K. Varshney

    Syracuse University, United States

    7 publications · 317 citations

    7

    h-index

  4. KD
    Klaus Dietmayer

    Universität Ulm, Germany

    6 publications · 278 citations

    6

    h-index

  5. NS
    Nageswara S. V. Rao

    Oak Ridge National Laboratory, United States

    6 publications · 167 citations

    6

    h-index

1–5 of 772

Venues

  1. 11

    h-index

  2. Information Fusion

    10 publications · 2.1K citations

    10

    h-index

  3. IEEE Transactions on Signal Processing

    8 publications · 479 citations

    8

    h-index

  4. 7

    h-index

  5. Robotics and Autonomous Systems

    5 publications · 373 citations

    5

    h-index

1–5