  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.
