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

care, dare.

History

Version
Description

1.0.0

initial version

Author

Allan CORNET

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