c4dynamics.filters.kalman.kalman.update

Contents

c4dynamics.filters.kalman.kalman.update#

kalman.update(z: ndarray, R: ndarray | None = None)[source]#

Updates (corrects) the state estimate based on the given measurements.

Parameters:

  • z (numpy.ndarray) – Measurement vector.

  • R (numpy.ndarray, optional) – Measurement noise covariance matrix. Defaults to None.

Returns:

K (numpy.ndarray) – Kalman gain.

Raises:

  • ValueError – If the number of elements in z does not match the number of rows in the measurement matrix H.

  • ValueError – If R is missing (neither provided during construction nor passed to update).

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 update step (update in steady-state mode where the measurement covariance matrix remains and is provided once during filter initialization):

>>> 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.update(z = 100)  # returns Kalman gain   
[[0.0156...]]
>>> kf.X                
[1.568...]
>>> kf.P                
[[3.187...]]

Update with modified measurement 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]]
>>> K = kf.update(z = 150, R = 0)
>>> K   
[[1]]
>>> kf.X  
[150]
>>> kf.P  
[[0]]
Contents