Noise Identification for Data-augmented Physics-based State-Space Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973101" target="_blank" >RIV/49777513:23520/24:43973101 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/SiPS62058.2024.00026" target="_blank" >https://doi.org/10.1109/SiPS62058.2024.00026</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SiPS62058.2024.00026" target="_blank" >10.1109/SiPS62058.2024.00026</a>
Alternative languages
Result language
angličtina
Original language name
Noise Identification for Data-augmented Physics-based State-Space Models
Original language description
This paper deals with the state-space modelling of nonlinear stochastic dynamic systems. The emphasis is laid on the emerging area of data-augmented physics-based modelling of the state dynamics, which combines the benefits of the physics-driven and data-based identified models. As the augmented state-space models depend on the measured data, modelling the state noise properties becomes challenging. This paper proposes and validates a concept for the state noise identification of nonlinear data-augmented state equation using the maximum likelihood and correlation-based methods. The numerical simulation of a tracking scenario shows significant improvement of the state estimation accuracy and consistency when using the identified noise model.
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/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE Workshop on Signal Processing Systems (SiPS)
ISBN
979-8-3503-7375-2
ISSN
1520-6130
e-ISSN
2374-7390
Number of pages
6
Pages from-to
101-106
Publisher name
IEEE
Place of publication
Piscataway
Event location
Cambridge, USA
Event date
Nov 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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