Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF

TL;DR

This paper introduces a sparsity-promoting joint square-root unscented Kalman filter that estimates system states and model uncertainties simultaneously, leading to more accurate and interpretable models in engineering applications.

Contribution

It presents a novel method combining sparsity promotion with a joint UKF to estimate states and model uncertainties together, enhancing interpretability and accuracy.

Findings

Reduced estimation error compared to traditional UKF

Enhanced physical interpretability of models

Effective selection of physics-motivated library functions

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.