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Home Page: https://xylambda.github.io/kalmankit/
License: Apache License 2.0
Multidimensional implementation of standard and extended Kalman Filters
Home Page: https://xylambda.github.io/kalmankit/
License: Apache License 2.0
The doc does not seem to render properly when in deployed:
https://xylambda.github.io/kalmanfilter/api.html
Extend testing coverage for EKF.
Hello,
I tried to import the KalmanFilter-function to run your example, but I got the error in the title.
I used
git clone https://github.com/Xylambda/kalmanfilter.git
pip install kalmanfilter/.
to install the package and run it on the following specs:
Windows 10
Python 3.9.2
Any idea why it didnt work?
Add support for the extended Kalman filter
https://en.wikipedia.org/wiki/Extended_Kalman_filter#Discrete-time_predict_and_update_equations
When calling filter
and then smooth
, the number of kalman gains is duplicated due to the variable being a list that is extended on each loop.
Solution: pre-allocate an array in the filter
method.
Implement RTS smoother for EKF.
Possible code:
def smooth(Z, U):
# allow U to be None without the filter failing
U = check_none_and_broadcast(U, Z)
# filtering process to get posteriors
x_est, P_est = self.filter(Z=Z, U=U)
# mean and covariance array allocation
xk_smooth = np.zeros((len(Z), self.state_size))
Pk_smooth = np.zeros((len(Z), self.state_size, self.state_size))
# smooth initialization
xk_smooth[-1] = x_est[-1]
Pk_smooth[-1] = P_est[-1]
n_obs = len(Z)
for k in range(n_obs - 2, -1, -1):
# select appropiate parameters for each time step
xk = x_est[k]
uk = U[k]
Qk = self.Q[k]
# predicted mean and covariance
xk_ahead = self.f(xk, uk) # mk is x_est[k]
Ak = self.jacobian_A(x_est[k], uk)
Pk_ahead = Ak @ (P_est[k] @ Ak.T) + Qk
# smooth (like butter) process
Kk = P_est[k] @ (Ak.T @ np.linalg.pinv(Pk_ahead))
xk_smooth[k] = x_est[k] + Kk @ (xk_smooth[k + 1] - xk_ahead)
Pk_smooth[k] = P_est[k] + Kk @ ((Pk_smooth[k + 1] - Pk_ahead.T) @ Kk)
return xk_smooth, Pk_smooth
I'm not happy with the current output format of the filter. It is a list of arrays, instead of an array with the values. I think I can use the state_size
attribute to allocate an array of the appropiate size.
Make B
and U
parameters optional.
@misc{alejandro2019kalmanfilter,
title={kalmanfilter},
author={Alejandro Pérez-Sanjuán},
year={2019},
howpublished={\url{https://github.com/Xylambda/kalmanfilter}},
}
Add support for the unscented Kalman Filter
Docs are not rendering well and they are not displaying math equations.
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