So in summary, the feel that can be applied to non-linear systems and we can do this when or if it
is possible to make appropriate assumptions or approximations which allow the nonlinear system to be
expressed in a linear form, the increased error in the modeling process will most likely be needed
to be accounted for by artificially increasing the process model noise.
This is going to be a trade off with performance because as we increase the process, more noise is
going to rely less on the prediction model, which means the field will be taking less prediction action
and relying more on the measurements itself.
The filter will also be needed to be tested and manually tuned to ensure the best performance.
There's also a possibility that the field will diverge from the truth.
And this is because of the numerical approximations that we made inside the modelling.
If there's too much error in the model where the model differs from the actual model in real life,
then these errors can build up and compound and cause the field of state to diverge.