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
Parents
97
The Labeled Multi-Bernoulli Filter
Stephan Reuter, Ba-Tuong Vo, Ba‐Ngu Vo et al. · IEEE Transactions on Signal Processing · 2014 · 790 citations
Robust Distributed Fusion With Labeled Random Finite Sets
Suqi Li, Wei Yi, Reza Hoseinnezhad et al. · IEEE Transactions on Signal Processing · 2017 · 132 citations · Full text
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Leibniz Institute for Plasma Science and Technology, Germany
11 publications · 1.4K citations
11
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1–5 of 163
67
Curtin University
Perth, Australia
18 h-index
58 publications
5.3K citations
RMIT University
Melbourne, Australia
14 h-index
47 publications
1.8K citations
Universität Ulm
Ulm, Germany
12 h-index
38 publications
3.4K citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
18 publications
590 citations

University of Electronic Science and Technology of China
Chengdu, China
6 h-index
17 publications
990 citations
University of Florence
Florence, Italy
5 h-index
9 publications
376 citations

The University of Western Australia
Perth, Australia
4 h-index
7 publications
243 citations

University of Calgary
Calgary, Canada
4 h-index
4 publications
159 citations
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20 publications · 2.5K citations
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9 publications · 251 citations
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7 publications · 280 citations
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6 publications · 105 citations
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5 publications · 78 citations
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