rlabbe
Improve IMM to handle EKF, and perhaps UKF
Right now the IMM hard codes in the predict step, which makes it suitable only for linear process models.
At the minimum, make a predict() function that can be overloaded. This will allow easy implementation of an EIMM via derivation and overloading, but ideally think about it a bit and come up with a scheme that works for UKFs as well.
Was this solved? Does filterpy allow for EIMM / UIMM or just IMM with linear Kalman filters?
It looks like this was solved but there are no callbacks for operations on x yet. So if you're working with things like angles it's not gonna work.
@M1cha I am curious why we could not get things working if we have angles? I am trying to feed a Constant Velocity (CV) and a Coordinated Turn (CT) model. The CT model deals with turn rate (angle) and it is non-linear but I might try using the examples in this notebook to design a class for the CT model. Is there something I missed?
@wang701 that's because they wrap around or in Rogers words "are modular" https://nbviewer.jupyter.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/10-Unscented-Kalman-Filter.ipynb#Implementation You can also check the *_fn arguments to the UKF code.
Angular velocities and angular accelerations should not be a problem because they don't wrap(you can have millions of rad/s), only angular positions are a problem.