kalman
Design Kalman filter for state estimation.
Syntax
[kalmf, L, P, M, Z] = kalman(sys, Q, R, N)
[kalmf, L, P, M, Z] = kalman(sys, Q, R, N, sensors, known)
Input argument
sys - Plant model with process noise: state-space model.
Q - Process noise covariance: scalar or matrix.
R - Measurement noise covariance: scalar or matrix.
N - Noise cross covariance: scalar or matrix.
sensors - Measured outputs of sys: vector.
known - Known inputs of sys: vector.
Output argument
kalmf - Kalman estimator: state-space model
L - Filter gains: matrix
P - Steady-state error covariances: matrix
M - Innovation gains of state estimators: matrix
Z - Steady-state error covariances: matrix
Description
[kalmf, L, P] = kalman(sys, Q, R, N) generates a Kalman filter using the provided plant model sys and noise covariance matrices Q, R, and N.
The function calculates a Kalman filter suitable for application in a Kalman estimator, as depicted in the following diagram.
Example
A = [11.269 -0.4940 1.129; 1.0000 0 0;0 1.0000 0];
B = [-0.3832; 0.5919; 0.5191];
C = [1 0 0];
sys = ss(A,[B, B], C, 0);
Q = 1;
R = 1;
[kEst, l, p, m, z] = kalman(sys, Q, R, [])See also
History
1.0.0
initial version
Author
Allan CORNET
Last updated
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