UnscentedTransform¶
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class
probnum.filtsmooth.
UnscentedTransform
(ndim, spread=0.0001, priorpar=2.0, special_scale=0.0)[source]¶ 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. unscented_weights
(alp, bet)See BFaS; p. Methods Documentation
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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_*”.
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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),
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sigma_points
(mean, covar)[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)
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