GHFilterOrder¶
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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class
filterpy.gh.
GHFilterOrder
(x0, dt, order, g, h=None, k=None)[source]¶ A g-h filter of aspecified order 0, 1, or 2.
Strictly speaking, the g-h filter is order 1, and the 2nd order filter is called the g-h-k filter. I’m not aware of any filter name that encompasses orders 0, 1, and 2 under one name, or I would use it.
Methods
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__init__
(x0, dt, order, g, h=None, k=None)[source]¶ Creates a g-h filter of order 0, 1, or 2.
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
Members
- self.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.
- self.residual : np.array
- difference between the measurement and the prediction
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