Matrices

The figure below shows a typical Matrix property page. The dimension of the matrices is automatically updated in accordance to the dimension of the corresponding vector properties.

Tip! If you have matrices on a tab separated format, e.g. in a spreadsheet or Matlab, you can copy these values into the clipboard and use the Paste button in the matrix dialog to set the value of the matrix.

Ak - System-transition matrix

This is the system transition matrix of the current model. If a nonlinear model is used, this matrix shows the state equation linearized with respect to the states.

Bk - System-control matrix

This is the system control matrix of the current model. If a nonlinear model is used, this matrix shows the state equation linearized with respect to the control values.

Ck - System-noise matrix

This is the system noise matrix of the current model.

Dk - Measurement-state matrix

This is the measurement-state matrix of the current model. If a nonlinear model is used, this matrix shows the output equation linearized with respect to the states.

Ek - Measurement-control matrix

This is the measurement control matrix of the current model. If a nonlinear model is used, this matrix shows the output equation linearized with respect to the control values.

Fk - System-disturbance matrix

This is the system disturbance matrix of the current model. If a nonlinear model is used, this matrix shows the state equation linearized with respect to the disturbances.

Gk - Measurement disturbance matrix

This is the measurement disturbance matrix of the current model. If a nonlinear model is used, this matrix shows the output equation linearized with respect to the disturbances.

Vk - Process noise cov. matrix

The process noise covariance matrix is used to define the covariance of the process noise vector.

VBiask - Bias noise cov. matrix

The bias noise covariance matrix is used to define the covariance of the bias noise vector (the augmented process noise vector). The sequence of the matrix elements is the same as the order the estimated biases appear in the measurement vector.

VPark - Param noise cov. matrix

The parameter noise covariance matrix is used to define the covariance of the parameter noise vector (the augmented process noise vector). The sequence of the matrix elements is the same as the order the estimated parameters appear in the parameter vector for nonlinear models.

Wk - Meas. noise cov. matrix

The output (measurement) noise covariance matrix is used to define the covariance of the measurement noise vector.

M0 - Init. state cov. matrix

This matrix defines the initial augmented state covariance matrix, i.e. the value of the state covariance matrix when the application is loaded or reset.

Pk - Filtered state cov. matrix

This is the current value of the filtered augmented state covariance matrix: 

Kk - Kalman gain matrix

The Kalman gain matrix shows the resulting augmented Kalman gain when the constant gain parameter is set to false. When the constant gain parameter is true, this matrix is used to define the Kalman gain.

A - complete

This is a read only matrix showing the augmented system-transition matrix including bias- and parameter estimation.

B - complete

This is a read only matrix showing the augmented system-control matrix including bias- and parameter estimation.

C - complete

This is a read only matrix showing the augmented system-noise matrix including bias- and parameter estimation.

D - complete

This is a read only matrix showing the augmented measurement-state matrix including bias- and parameter estimation.

F - complete

This is a read only matrix showing the augmented system-disturbance matrix including bias- and parameter estimation.

V - complete

This is a read only matrix showing the augmented system state covariance matrix including bias- and parameter estimation.