Labeled Multi-bernoulli Filtering

Labeled Multi-Bernoulli Filtering is a Bayesian filtering framework for multi-target tracking that models the collection of targets as a labeled multi-Bernoulli random finite set, thereby enabling the joint estimation of target states and their persistent identities while distinguishing newly detected targets from pre-existing ones.

97

Publications

4.5K

Citations

163

Authors

67

Institutions

Publications per year

2017–2026

70

Variants

Lmb Filtering

Authors

163

  1. BV
    Ba-Tuong Vo

    Curtin University, Australia

    18 publications · 2.2K citations

    16

    h-index

  2. BV
    Ba‐Ngu Vo

    Curtin University, Australia

    18 publications · 2.2K citations

    15

    h-index

  3. RH
    Reza Hoseinnezhad

    RMIT University, Australia

    15 publications · 654 citations

    14

    h-index

  4. AK
    Amirali Khodadadian Gostar

    RMIT University, Australia

    10 publications · 368 citations

    11

    h-index

  5. SR
    Stephan Reuter

    Leibniz Institute for Plasma Science and Technology, Germany

    11 publications · 1.4K citations

    11

    h-index

1–5 of 163

Venues

  1. IEEE Transactions on Signal Processing

    20 publications · 2.5K citations

    18

    h-index

  2. Signal Processing

    9 publications · 251 citations

    9

    h-index

  3. 8

    h-index

  4. Sensors

    6 publications · 105 citations

    6

    h-index

  5. IEEE Access

    5 publications · 78 citations

    5

    h-index

1–5