State-Space Parameter Estimation is a methodological framework within parameter estimation under uncertainty that specifically involves inferring model parameters by jointly modeling the latent state dynamics and观测 processes within a state-space representation, thereby distinguishing it from conventional parameter estimation approaches that do not explicitly separate system states from observation mechanisms.
407
Publications
22.7K
Citations
908
Authors
476
Institutions
Publications per year
2017–2026
70
Publications
407
1–5 of 407
Authors
908
- FD
17
h-index
- AD
8
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- TH
7
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- YG
7
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- LX
5
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1–5 of 908
Institutions
476
Jiangnan University
Wuxi, China
20 h-index
59 publications
3.7K citations
Centre National de la Recherche Scientifique
Paris, France
10 h-index
23 publications
1.5K citations
University of Alberta
Edmonton, Canada
8 h-index
15 publications
1K citations
University of Cambridge
Cambridge, United Kingdom
7 h-index
12 publications
1.4K citations
Qingdao University of Science and Technology
Qingdao, China
7 h-index
10 publications
898 citations
University of Southern California
Los Angeles, United States
7 h-index
13 publications
634 citations
King Abdulaziz University
Jeddah, Saudi Arabia
7 h-index
16 publications
1K citations
University of Waterloo
Waterloo, Canada
6 h-index
10 publications
398 citations
1–8 of 476
Venues
- Automatica
17 publications · 1.8K citations
15
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- IEEE Transactions on Automatic Control
17 publications · 913 citations
14
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- IFAC Proceedings Volumes
13 publications · 529 citations
12
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- Journal of the Franklin Institute
10 publications · 386 citations
10
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- IEEE Transactions on Signal Processing
9 publications · 756 citations
9
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1–5