Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962470" target="_blank" >RIV/49777513:23520/21:43962470 - isvavai.cz</a>
Result on the web
<a href="https://dx.doi.org/10.1109/MFI52462.2021.9591173" target="_blank" >https://dx.doi.org/10.1109/MFI52462.2021.9591173</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MFI52462.2021.9591173" target="_blank" >10.1109/MFI52462.2021.9591173</a>
Alternative languages
Result language
angličtina
Original language name
Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations
Original language description
Mean square error matrices belong to key concepts in decentralised estimation. They assess the quality of estimates and are essential for the optimisation of the estimation fusion. In the case of a missing knowledge, sets of admissible matrices are replaced by their bounds and a robust fusion is applied. This paper prospects a specific partial knowledge of the sets of matrices. Upper bounds are constructed first. Then, the stress is laid on non-zero lower bounds, which do not exist in the fusion under completely unknown correlation. Limit cases are discussed on numerical examples and graphical illustrations are given.
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 International Conference on Multisensor Fusion and Integration (MFI 2021)
ISBN
978-1-66544-521-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Karlsruhe, Germany
Event location
Karlsruhe, Německo
Event date
Sep 23, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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