unscented_transform¶
Copyright 2015 Roger R Labbe Jr.
FilterPy library. http://github.com/rlabbe/filterpy
Documentation at: https://filterpy.readthedocs.org
Supporting book at: https://github.com/rlabbe/KalmanandBayesianFiltersinPython
This is licensed under an MIT license. See the readme.MD file for more information.

filterpy.kalman.
unscented_transform
(sigmas, Wm, Wc, noise_cov=None, mean_fn=None, residual_fn=None)[source]¶ Computes unscented transform of a set of sigma points and weights. returns the mean and covariance in a tuple.
This works in conjunction with the UnscentedKalmanFilter class.
Parameters:  sigmas: ndarray, of size (n, 2n+1)
2D array of sigma points.
 Wm : ndarray [# sigmas per dimension]
Weights for the mean.
 Wc : ndarray [# sigmas per dimension]
Weights for the covariance.
 noise_cov : ndarray, optional
noise matrix added to the final computed covariance matrix.
 mean_fn : callable (sigma_points, weights), optional
Function that computes the mean of the provided sigma points and weights. Use this if your state variable contains nonlinear values such as angles which cannot be summed.
def state_mean(sigmas, Wm): x = np.zeros(3) sum_sin, sum_cos = 0., 0. for i in range(len(sigmas)): s = sigmas[i] x[0] += s[0] * Wm[i] x[1] += s[1] * Wm[i] sum_sin += sin(s[2])*Wm[i] sum_cos += cos(s[2])*Wm[i] x[2] = atan2(sum_sin, sum_cos) return x
 residual_fn : callable (x, y), optional
Function that computes the residual (difference) between x and y. You will have to supply this if your state variable cannot support subtraction, such as angles (3591 degreees is 2, not 358). x and y are state vectors, not scalars.
def residual(a, b): y = a[0]  b[0] y = y % (2 * np.pi) if y > np.pi: y = 2*np.pi return y
Returns:  x : ndarray [dimension]
Mean of the sigma points after passing through the transform.
 P : ndarray
covariance of the sigma points after passing throgh the transform.
Examples
See my book Kalman and Bayesian Filters in Python https://github.com/rlabbe/KalmanandBayesianFiltersinPython