@inproceedings{34171,
  abstract     = {{State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}},
  keywords     = {{joint estimation, unscented transform, Kalman filter, sparsity, data-driven, compressed sensing}},
  location     = {{Canberra, Australien}},
  number       = {{1}},
  pages        = {{85--90}},
  title        = {{{Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}}},
  doi          = {{https://doi.org/10.1016/j.ifacol.2023.02.015}},
  volume       = {{56}},
  year         = {{2023}},
}