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 Arguments

Parameter	Description
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 Arguments

Parameter	Description
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.

💡Examples

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

care dare

🕔Version History

Version	Description
1.0.0	initial version

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