Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962473" target="_blank" >RIV/49777513:23520/21:43962473 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9626987" target="_blank" >https://ieeexplore.ieee.org/document/9626987</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values
Original language description
This paper is devoted to the Bayesian state estimation of the nonlinear stochastic dynamic systems. The stress is laid on Gaussian unscented Kalman filter (UKF) and, in particular, on a setting of its scaling parameter, which significantly affects the UKF estimation performance. Compared to the standard UKF design, where one scaling parameter per a time instant is selected, the proposed cooperative UKF combines estimates of the set of UKFs each designed with different value of the scaling parameter. The cooperative UKF reformulates the UKF scaling parameter selection task as the multiple model approach, which allows to extract more information from the measurement to provide estimates of better quality as indicated by the numerical simulations.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GC20-06054J" target="_blank" >GC20-06054J: Intelligent Distributed Estimation Architectures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2021 IEEE 24th International Conference on Information Fusion (FUSION)
ISBN
978-1-73774-971-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Sun City
Event location
Sun City, Jihoafrická republika
Event date
Nov 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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