c4dynamics.filters.kalman.kalman.predict

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c4dynamics.filters.kalman.kalman.predict#

kalman.predict(u: ndarray | None = None, Q: ndarray | None = None)[source]#

Predicts the filter’s next state and covariance matrix based on the current state and the process model.

Parameters:

  • u (numpy.ndarray, optional) – Control input. Defaults to None.

  • Q (numpy.ndarray, optional) – Process noise covariance matrix. Defaults to None.

Raises:

  • ValueError – If Q is not missing (neither provided during construction nor passed to predict).

  • ValueError – If a control input is provided, but the number of elements in u does not match the number of columns of the input matrix G.

Examples

For more detailed usage, see the examples in the introduction to the filters module and the kalman class.

Import required packages:

>>> from c4dynamics.filters import kalman

Plain predict step (predict in steady-state mode where the process variance matrix remains constant and is provided once to initialize the filter):

>>> kf = kalman({'x': 0}, P0 = 0.5**2, F = 1, H = 1, Q = 0.05, R = 200, steadystate = True)
>>> print(kf)
[ x ]
>>> kf.X          
[0]
>>> kf.P          
[[3.187...]]
>>> kf.predict()
>>> kf.X          
[0]
>>> kf.P          
[[3.187...]]

Predict with control input:

>>> kf = kalman({'x': 0}, P0 = 0.5**2, F = 1, G = 150, H = 1, R = 200, Q = 0.05)
>>> kf.X  
[0]
>>> kf.P          
[[0.25...]]
>>> kf.predict(u = 1)
>>> kf.X      
[150]
>>> kf.P  
[[0.3]]

Predict with updated process noise covariance matrix:

>>> kf = kalman({'x': 0}, P0 = 0.5**2, F = 1, G = 150, H = 1, R = 200, Q = 0.05)
>>> kf.X  
[0]
>>> kf.P 
[[0.25]]
>>> kf.predict(u = 1, Q = 0.01)
>>> kf.X  
[150]
>>> kf.P 
[[0.26]]
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