least_squares_parameters¶
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.gh.
least_squares_parameters
(n)[source]¶ An order 1 least squared filter can be computed by a gh filter by varying g and h over time according to the formulas below, where the first measurement is at n=0, the second is at n=1, and so on:
\[ \begin{align}\begin{aligned}h_n = \frac{6}{(n+2)(n+1)}\\g_n = \frac{2(2n+1)}{(n+2)(n+1)}\end{aligned}\end{align} \]Parameters:  n : int
the nth measurement, starting at 0 (i.e. first measurement has n==0)
Returns:  (g,h) : (float, float)
g and h parameters for this time step for the leastsquares filter
Examples
from filterpy.gh import GHFilter, least_squares_parameters lsf = GHFilter (0, 0, 1, 0, 0) z = 10 for i in range(10): g,h = least_squares_parameters(i) lsf.update(z, g, h)