# UnscentedTransform¶

Bases: object

Used for unscented Kalman filter.

Parameters
• dimension (int) – Spatial dimensionality

• priorpar (float) – Incorporate prior knowledge about distribution of x. For Gaussians, 2.0 is optimal (see link below)

• special_scale (float) – Secondary scaling parameter. The primary parameter is computed below.

References

1

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.

Methods Documentation

estimate_statistics(proppts, sigpts, covmat, mpred)[source]

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)[source]

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(rv)[source]

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)