Parallel Particle Filtering
Parallel Particle Filtering is a computational framework within the class of particle filtering methods that employs concurrent processing of multiple particles or particle sets across distributed or multi-core architectures to enhance estimation accuracy and reduce computational latency in nonlinear, non-Gaussian state-space models.
64
Publications
2.4K
Citations
162
Authors
92
Institutions
Publications per year
2017–2026
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Stony Brook University
Stony Brook, United States
6 h-index
15 publications
1.2K citations

Linköping University
Linköping, Sweden
4 h-index
11 publications
437 citations

McMaster University
Hamilton, Canada
3 h-index
7 publications
252 citations

Zhejiang University
Hangzhou, China
3 h-index
9 publications
227 citations

Ghent University
Ghent, Belgium
2 h-index
2 publications
82 citations

University of Ottawa
Ottawa, Canada
2 h-index
3 publications
61 citations

Cairo University
Giza, Egypt
2 h-index
2 publications
36 citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
7 publications
225 citations
1–8 of 92
5 publications · 435 citations
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PIER · 2 publications · 37 citations
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2 publications · 101 citations
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2 publications · 36 citations
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1 publications · 140 citations
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