---
_id: '44326'
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."
author:
- first_name: Ricarda-Samantha
  full_name: Götte, Ricarda-Samantha
  id: '43992'
  last_name: Götte
- first_name: Julia
  full_name: Timmermann, Julia
  id: '15402'
  last_name: Timmermann
citation:
  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} }'
  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.'
conference:
  end_date: 2023-07-14
  location: Yokohama, Japan
  name: 22nd IFAC World Congress
  start_date: 2023-07-09
date_created: 2023-05-02T15:16:43Z
date_updated: 2024-11-13T08:42:37Z
department:
- _id: '153'
- _id: '880'
intvolume: '        56'
issue: '2'
keyword:
- joint estimation
- unscented Kalman filter
- sparsity
- Laplacian prior
- regularized horseshoe
- principal component analysis
language:
- iso: eng
page: 869-874
publication: IFAC-PapersOnLine
quality_controlled: '1'
status: public
title: Approximating a Laplacian Prior for Joint State and Model Estimation within
  an UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...