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arXiv · 2207.03916

Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF

Ricarda-Samantha Götte·Julia Timmermann
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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.

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BibTeXRIS

Ricarda-Samantha Götte, Julia Timmermann. 2022-07-08. Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. https://arxiv.org/abs/2207.03916

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