Bayesian Data Association

Bayesian Data Association is a probabilistic data association framework that employs Bayesian inference to compute the posterior probabilities of associations between observed measurements and target tracks, distinguishing itself by rigorously incorporating prior knowledge and uncertainty in dynamic multi-object environments.

89

Publications

10.7K

Citations

201

Authors

105

Institutions

Publications per year

2017–2026

13

Authors

201

  1. WK
    Wolfgang Koch

    Fraunhofer Institute for Communication, Information Processing and Ergonomics, Germany

    6 publications · 567 citations

    6

    h-index

  2. BV
    Ba‐Ngu Vo

    Curtin University, Australia

    4 publications · 218 citations

    4

    h-index

  3. LS
    Lennart Svensson

    Chalmers University of Technology, Sweden

    3 publications · 55 citations

    3

    h-index

  4. FM
    Florian Meyer

    TU Wien, Austria

    3 publications · 82 citations

    3

    h-index

  5. MF
    Michael Feldmann

    Fraunhofer Institute for Communication, Information Processing and Ergonomics, Germany

    2 publications · 534 citations

    2

    h-index

1–5 of 201

Venues

  1. 7

    h-index

  2. IEEE Transactions on Signal Processing

    5 publications · 642 citations

    5

    h-index

  3. 4

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 75 citations

    3

    h-index

  5. Automatica

    2 publications · 46 citations

    2

    h-index

1–5