Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF
R.-S. Götte, J. Timmermann, in: Accepted for IFAC World Congress 2023, 2023.
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Abstract
Low-quality models that miss relevant dynamics lead to major challenges in modelbased
state estimation. We address this issue by simultaneously estimating the system’s states
and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,
we augment the state with the parameter vector of a linear combination containing suitable
functions that approximate the lacking dynamics. Presuming that only a few dynamical terms
are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like
sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace
distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,
the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is
applied instead. Results exhibit small estimation errors with model improvements detected by
an automated model reduction technique.
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Publishing Year
Proceedings Title
Accepted for IFAC World Congress 2023
Conference Location
Yokohama, Japan
Conference Date
2023-07-08 – 2023-07-14
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Cite this
Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. In: Accepted for IFAC World Congress 2023. ; 2023.
Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. Accepted for IFAC World Congress 2023.
@inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}, booktitle={Accepted for IFAC World Congress 2023}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023} }
Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” In Accepted for IFAC World Congress 2023, 2023.
R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF,” Yokohama, Japan, 2023.
Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” Accepted for IFAC World Congress 2023, 2023.