Introduction - If you have any usage issues, please Google them yourself
Design of 5.4.2 Kalman filter
This section will discuss how to use the control system toolbox to design and simulate Kalman filters. Consider the following discrete systems:
X [n+1] =Ax [n] +B (u [n] +w [n]) (5.9)
Y [n] =Cx [n] (5.10)
Among them, w [n] is the Gauss noise added at the input end. State matrix parameters are respectively
A = [1.1269-0.49400.1129
1
1];
B = [-0.3832
Zero point five nine one nine
0.5191];
C = [100];
Our goal is to design Kalman filters at given input u [n] and noise output measurements.
The output of the system is estimated in the case of YV [n] =Cx [n] +v [n]. Among them, v [n] is Gauss white noise.
1) discrete Kalman filter
The steady-state Kalman filter equations for the above problems are as follows:
Correction calculation of measurement value