benedict_bornder_constants¶
Copyright 2014 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.
benedict_bornder_constants
(g, critical=False)[source]¶ Computes the g,h constants for a Benedict-Bordner filter, which minimizes transient errors for a g-h filter.
Returns the values g,h for a specified g. Strictly speaking, only h is computed, g is returned unchanged.
The default formula for the Benedict-Bordner allows ringing. We can “nearly” critically damp it; ringing will be reduced, but not entirely eliminated at the cost of reduced performance.
Parameters
- g : float
- scaling factor g for the filter
- critical : boolean, default False
- Attempts to critically damp the filter.
Returns
- g : float
- scaling factor g (same as the g that was passed in)
- h : float
- scaling factor h that minimizes the transient errors
Example:
from filterpy.gh import GHFilter, benedict_bornder_constants g, h = benedict_bornder_constants(.855) f = GHFilter(0, 0, 1, g, h)
References
Brookner, “Tracking and Kalman Filters Made Easy”. John Wiley and Sons, 1998.