Distributed Particle Filtering is a distributed state estimation method that extends conventional particle filtering by partitioning the filtering task across multiple processing nodes, each operating on a subset of particles and collaboratively converging toward a global state estimate through localized communication and fusion.
115
Publications
5.8K
Citations
255
Authors
140
Institutions
Publications per year
2017–2026
20
Parents
Publications
115
1–5 of 115
Authors
255
- FH
11
h-index
- PM
11
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- OH
9
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- AA
8
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- AM
8
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1–5 of 255
Institutions
140
TU Wien
Vienna, Austria
11 h-index
32 publications
2.2K citations
Stony Brook University
Stony Brook, United States
11 h-index
17 publications
1.5K citations
York University
Toronto, Canada
8 h-index
16 publications
489 citations
McGill University
Montreal, Canada
7 h-index
15 publications
763 citations
Instituto Tecnológico de Aeronáutica
São José dos Campos, Brazil
5 h-index
8 publications
214 citations
Universidad Carlos III de Madrid
Madrid, Spain
3 h-index
3 publications
189 citations
Dalian University of Technology
Dalian, China
3 h-index
9 publications
180 citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
3 publications
51 citations
1–8 of 140
Venues
- IEEE Transactions on Signal Processing
11 publications · 1.4K citations
11
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- IEEE Transactions on Signal and Information Processing over Networks
5 publications · 141 citations
5
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- Signal Processing
4 publications · 124 citations
4
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- IEEE Signal Processing Letters
3 publications · 70 citations
3
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- IEEE Signal Processing Magazine
2 publications · 312 citations
2
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