optimal_noise_smoothing¶
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/Kalman-and-Bayesian-Filters-in-Python
This is licensed under an MIT license. See the readme.MD file for more information.
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filterpy.gh.
optimal_noise_smoothing
(g)[source]¶ provides g,h,k parameters for optimal smoothing of noise for a given value of g. This is due to Polge and Bhagavan[1].
Parameters: - g : float
value for g for which we will optimize for
Returns: - (g,h,k) : (float, float, float)
values for g,h,k that provide optimal smoothing of noise
References
[1] Polge and Bhagavan. “A Study of the g-h-k Tracking Filter”. Report No. RE-CR-76-1. University of Alabama in Huntsville. July, 1975
Examples
from filterpy.gh import GHKFilter, optimal_noise_smoothing g,h,k = optimal_noise_smoothing(g) f = GHKFilter(0,0,0,1,g,h,k) f.update(1.)