GHFilterOrder¶
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.

class
filterpy.gh.
GHFilterOrder
(x0, dt, order, g, h=None, k=None)[source]¶ A gh filter of aspecified order 0, 1, or 2.
Strictly speaking, the gh filter is order 1, and the 2nd order filter is called the ghk filter. I’m not aware of any filter name that encompasses orders 0, 1, and 2 under one name, or I would use it.
Parameters:  x0 : 1D np.array or scalar
Initial value for the filter state. Each value can be a scalar or a np.array.
You can use a scalar for x0. If order > 0, then 0.0 is assumed for the higher order terms.
x[0] is the value being tracked x[1] is the first derivative (for order 1 and 2 filters) x[2] is the second derivative (for order 2 filters)
 dt : scalar
timestep
 order : int
order of the filter. Defines the order of the system 0  assumes system of form x = a_0 + a_1*t 1  assumes system of form x = a_0 +a_1*t + a_2*t^2 2  assumes system of form x = a_0 +a_1*t + a_2*t^2 + a_3*t^3
 g : float
filter g gain parameter.
 h : float, optional
filter h gain parameter, order 1 and 2 only
 k : float, optional
filter k gain parameter, order 2 only
 Atrributes
 ——
 x : np.array
State of the filter.
x[0] is the value being tracked x[1] is the derivative of x[0] (order 1 and 2 only) x[2] is the 2nd derivative of x[0] (order 2 only)
This is always an np.array, even for order 0 where you can initialize x0 with a scalar.
 y : np.array
Residual  difference between the measurement and the prediction
 dt : scalar
timestep
 order : int
order of the filter. Defines the order of the system 0  assumes system of form x = a_0 + a_1*t 1  assumes system of form x = a_0 +a_1*t + a_2*t^2 2  assumes system of form x = a_0 +a_1*t + a_2*t^2 + a_3*t^3
 g : float
filter g gain parameter.
 h : float
filter h gain parameter, order 1 and 2 only
 k : float
filter k gain parameter, order 2 only
 z : 1D np.array or scalar
measurement passed into update()