Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359339" target="_blank" >RIV/68407700:21230/22:00359339 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21720/22:00359339
Result on the web
<a href="https://doi.org/10.23919/FUSION49751.2022.9841381" target="_blank" >https://doi.org/10.23919/FUSION49751.2022.9841381</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION49751.2022.9841381" target="_blank" >10.23919/FUSION49751.2022.9841381</a>
Alternative languages
Result language
angličtina
Original language name
Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights
Original language description
The desensitized Kalman filter can robustly estimate the state of a system with uncertain parameters without knowledge about uncertainty type. In this paper, the desensitized Kalman filter for nonlinear systems is derived using Taylor series expansion and a stochastic approach to reduce estimation error sensitivity to uncertain parameters. Adaptively normalized weights tune the trade-off between the minimum uncertainty sensitivity and minimum mean square error. Among the main benefits of the algorithm are intuitive tuning concerning uncertainty and a form resembling the classical Riccati equation. The comparison to other robust state-of-the-art algorithms is discussed based on a numerical example.
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/CK03000269" target="_blank" >CK03000269: Advanced methods for on-board data processing in V2X systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 25th International Conference on Information Fusion (FUSION)
ISBN
978-1-7377497-2-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Linköping
Event date
Jul 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000855689000151