Bayesian Sensor Fusion

Bayesian Sensor Fusion is a probabilistic integration framework within sensor fusion that employs Bayesian inference to recursively update the belief about a system's state by combining prior knowledge with new sensor observations, distinguishing itself through its rigorous treatment of uncertainty and sequential data assimilation.

774

Publications

38.8K

Citations

2.1K

Authors

750

Institutions

Publications per year

2017–2026

222

Authors

2.1K

  1. RH
    Reza Hoseinnezhad

    RMIT University, Australia

    13 publications · 697 citations

    13

    h-index

  2. RM
    Ronald Mahler

    Lockheed Martin (United States), United States

    13 publications · 480 citations

    12

    h-index

  3. PW
    Peter Willett

    University of Connecticut, United States

    8 publications · 247 citations

    8

    h-index

  4. PB
    Paolo Braca

    North Atlantic Treaty Organization, Belgium

    8 publications · 291 citations

    8

    h-index

  5. WY
    Wei Yi

    University of Electronic Science and Technology of China, China

    8 publications · 427 citations

    8

    h-index

1–5 of 2.1K

Venues

  1. 23

    h-index

  2. Information Fusion

    22 publications · 1.7K citations

    21

    h-index

  3. IEEE Transactions on Signal Processing

    18 publications · 1.4K citations

    18

    h-index

  4. Signal Processing

    16 publications · 850 citations

    16

    h-index

  5. 15

    h-index

1–5