conference paper yes Ricarda-Samantha Götte author 43992 Julia Timmermann author 15402 153 department 880 department 22nd IFAC World Congress 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. 2023Yokohama, Japan eng joint estimationunscented Kalman filtersparsityLaplacian priorregularized horseshoeprincipal component analysis 562869-874 R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF,” in <i>IFAC-PapersOnLine</i>, Yokohama, Japan, 2023, vol. 56, no. 2, pp. 869–874. Götte, R.-S., &#38; Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. <i>IFAC-PapersOnLine</i>, <i>56</i>(2), 869–874. R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874. Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” In <i>IFAC-PapersOnLine</i>, 56:869–74, 2023. Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” <i>IFAC-PapersOnLine</i>, vol. 56, no. 2, 2023, pp. 869–74. @inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}, volume={56}, number={2}, booktitle={IFAC-PapersOnLine}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={869–874} } Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. In: <i>IFAC-PapersOnLine</i>. Vol 56. ; 2023:869-874. 443262023-05-02T15:16:43Z2024-11-13T08:42:37Z