Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Reliability, Safety and Security |
| ISBN (elektronisch) | 978-83-68136-08-1 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Veranstaltung | 34th European Safety and Reliability Conference - Jagiellonian University, Cracow, Polen Dauer: 23 Juni 2024 → 27 Juni 2024 Konferenznummer: 34 |
Publikationsreihe
| Name | ESREL 2024 Monograph Book Series |
|---|---|
| Band | 9 |
Abstract
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Advances in Reliability, Safety and Security. 2024. (ESREL 2024 Monograph Book Series ; Band 9).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Transport Map Coupling Filter for State-Parameter Estimation
AU - Grashorn, Jan
AU - Broggi, Matteo
AU - Chamoin, Ludovic
AU - Beer, Michael
N1 - Conference code: 34
PY - 2024
Y1 - 2024
N2 - Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a physical model is a common task for which filtering algorithms, such as Kalman filters and their non-linear extensions, are typically used. However, many of these filters use assumptions on the transition probabilities or the covariance model, which can lead to inaccuracies in non-linear systems. We will show the application of a stochastic coupling filter that can approximate arbitrary transition densities under non-Gaussian noise. The filter is based on transport maps, which couple the approximation densities to a user-chosen reference density, allowing for straightforward sampling and evaluation of probabilities.
AB - Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a physical model is a common task for which filtering algorithms, such as Kalman filters and their non-linear extensions, are typically used. However, many of these filters use assumptions on the transition probabilities or the covariance model, which can lead to inaccuracies in non-linear systems. We will show the application of a stochastic coupling filter that can approximate arbitrary transition densities under non-Gaussian noise. The filter is based on transport maps, which couple the approximation densities to a user-chosen reference density, allowing for straightforward sampling and evaluation of probabilities.
KW - eess.SP
KW - cs.SY
KW - eess.SY
U2 - 10.48550/arXiv.2407.02198
DO - 10.48550/arXiv.2407.02198
M3 - Conference contribution
SN - 978-83-68136-21-0
T3 - ESREL 2024 Monograph Book Series
BT - Advances in Reliability, Safety and Security
T2 - 34th European Safety and Reliability Conference
Y2 - 23 June 2024 through 27 June 2024
ER -