Sigma-point Filtering
Sigma-Point Filtering is a Bayesian filtering method that belongs to the class of numerical approximation techniques for nonlinear state estimation, distinguished by its use of a deterministic set of weighted sample points—sigma points—to approximate the mean and covariance of probability distributions through the unscented transformation, thereby enabling accurate propagation of uncertainty in nonlinear systems.
143
Publications
10.6K
Citations
314
Authors
146
Institutions
Publications per year
2017–2026
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146
Oregon Health & Science University
Portland, United States
7 h-index
12 publications
2.8K citations
Aalto University
Espoo, Finland
7 h-index
14 publications
480 citations

Southeast University
Nanjing, China
5 h-index
12 publications
543 citations
Chalmers University of Technology
Gothenburg, Sweden
5 h-index
10 publications
506 citations

Harbin Engineering University
Harbin, China
4 h-index
14 publications
290 citations
Jiangsu University
Zhenjiang, China
4 h-index
12 publications
488 citations
University of West Bohemia in Pilsen
Pilsen, Czechia
4 h-index
12 publications
288 citations

Cornell University
Ithaca, United States
3 h-index
5 publications
160 citations
1–8 of 146
6 publications · 834 citations
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4 publications · 634 citations
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Conference · 4 publications · 474 citations
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4 publications · 450 citations
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4 publications · 55 citations
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