UnscentedTransform

class probnum.filtsmooth.UnscentedTransform(dimension, spread=0.0001, priorpar=2.0, special_scale=0.0)

Bases: object

Used for unscented Kalman filter.

Methods Summary

estimate_statistics(proppts, sigpts, covmat, …)	Computes predicted summary statistics, predicted mean/kernels/crosscovariance, from (propagated) sigmapoints.
propagate(time, sigmapts, modelfct)	Propagate sigma points.
sigma_points(mean, covar)	Sigma points.

Methods Documentation

estimate_statistics(proppts, sigpts, covmat, mpred)

Computes predicted summary statistics, predicted mean/kernels/crosscovariance, from (propagated) sigmapoints.

Not to be confused with mean and kernels resulting from the prediction step of the Bayesian filter. Hence we call it “estimate_*” instead of “predict_*”.

propagate(time, sigmapts, modelfct)

Propagate sigma points.

Parameters:

• time (float) – Time \(t\) which is passed on to the modelfunction.
• sigmapts (np.ndarray, shape=(2 N+1, N)) – Sigma points (N is the spatial dimension of the dynamic model)
• modelfct (callable, signature=(t, x, **kwargs)) – Function through which to propagate

Returns:

M is the dimension of the measurement model

Return type:

np.ndarray, shape=(2 N + 1, M),

sigma_points(mean, covar)

Sigma points.

Parameters:

• mean (np.ndarray, shape (d,)) – mean of Gaussian distribution
• covar (np.ndarray, shape (d, d)) – kernels of Gaussian distribution

Returns:

Return type:

np.ndarray, shape (2 * d + 1, d)