Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.
The example given on the Kalman Filter documentation page defines a position+velocity problem, with this state transition matrix F:
f.F = np.array([[1.,1.],
[0.,1.]])
From what I can tell, the upper right element should actually be dt, not 1. I can exclude that the timestep size dt is coincidentally 1, because further down we have f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13).
The example given on the Kalman Filter documentation page defines a position+velocity problem, with this state transition matrix F:
From what I can tell, the upper right element should actually be
dt, not 1. I can exclude that the timestep sizedtis coincidentally 1, because further down we havef.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13).So, afaict, here it should say