Hi,
Only the measurements have a theoretical value (from the measurement model and the estimated orbit) and a real one (from the input observations). The difference between the two is called residuals and for Kalman filters, there is two types: pre and post fit, which are respectively from pure propagation and after the correction. In a way, you can see the propagation model parameters like drag coefficients as dynamical ones with vanishing rates. Compared to batch estimators, dynamical ones also consider process noise, so a source of uncertainty in between observations, which is a way of considering unmodelled effects but can also lead to divergence if too big for example.
Now, the correction uses the prefit residuals but also the covariance matrix, whose computation depends on the algorithm (extended, unscented). If there are correlations between parameters, they might get updated even if they were not observable through a particular measurement. Let’s remember that for linear systems, kalman filters and least squares are basically equivalent.
On another note, in Orekit, Kalman filters don’t have any smoothing at the end, so the first points suffer from poorer estimation than the last ones. Adding some algorithms to the library would actually be great.
I’ll let @markrutten add/correct something if he likes
Cheers,
Romain.