Distributed Bayesian Filtering

Distributed Bayesian Filtering is a Bayesian filtering framework in which multiple agents or nodes collaboratively estimate a shared state by locally processing data and exchanging probabilistic information over a network, distinguished from centralized or single-agent approaches by its decentralized computational architecture and consensus-based belief updating.

27

Publications

1.8K

Citations

68

Authors

44

Institutions

Publications per year

2017–2026

11

Authors

68

  1. TL
    Tiancheng Li

    Northwestern Polytechnical University, China

    3 publications · 180 citations

    3

    h-index

  2. CF
    Claudio Fantacci

    University of Florence, Italy

    2 publications · 370 citations

    2

    h-index

  3. GB
    Giorgio Battistelli

    University of Florence, Italy

    2 publications · 370 citations

    2

    h-index

  4. BW
    Bailu Wang

    University of Electronic Science and Technology of China, China

    2 publications · 58 citations

    2

    h-index

  5. AF
    Alfonso Farina

    SELEX Sistemi Integrati, Italy

    2 publications · 370 citations

    2

    h-index

1–5 of 68

Venues

  1. Information Fusion

    2 publications · 160 citations

    2

    h-index

  2. IET Radar Sonar & Navigation

    2 publications · 62 citations

    2

    h-index

  3. IEEE Transactions on Signal Processing

    2 publications · 149 citations

    2

    h-index

  4. 2

    h-index

  5. Signal Processing

    2 publications · 39 citations

    2

    h-index

1–5