{"citation":{"chicago":"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.","ieee":"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.","mla":"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.","short":"R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.","ama":"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.","apa":"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.","bibtex":"@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} }"},"_id":"44326","quality_controlled":"1","author":[{"last_name":"Götte","first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"page":"869-874","year":"2023","date_updated":"2024-11-13T08:42:37Z","volume":56,"issue":"2","language":[{"iso":"eng"}],"title":"Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF","status":"public","department":[{"_id":"153"},{"_id":"880"}],"publication":"IFAC-PapersOnLine","date_created":"2023-05-02T15:16:43Z","user_id":"43992","keyword":["joint estimation","unscented Kalman filter","sparsity","Laplacian prior","regularized horseshoe","principal component analysis"],"type":"conference","intvolume":"        56","abstract":[{"lang":"eng","text":"Low-quality models that miss relevant dynamics lead to major challenges in modelbased\r\nstate estimation. We address this issue by simultaneously estimating the system’s states\r\nand its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector of a linear combination containing suitable\r\nfunctions that approximate the lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe regularized horseshoe distribution, a Gaussian that approximately features sparsity, is\r\napplied instead. Results exhibit small estimation errors with model improvements detected by\r\nan automated model reduction technique."}],"conference":{"name":"22nd IFAC World Congress","location":"Yokohama, Japan","end_date":"2023-07-14","start_date":"2023-07-09"}}