Vectors

The general Kalman filter vector dialog has the following columns:

  • Name - The name of the vector element.
  • Value - The value of the vector element.
  • EngUnit - The engineering unit of the vector element
  • Range - The operating range of the vector element. This value is used to determine the perturbation steps for linearization and to scale the vectors for observability checking.
  • MaxVal - The maximum value of the vector element. For input vectors, values higher than this value are rejected.
  • MinVal - The minimum value of the vector element. For input vectors, values lower than this value are rejected.

When configuring the Kalman filter, the system dimension is set in these vector dialogs. The following sections describes the available vectors.

x0-init. state

Initial state for the Kalman filter. The filter starts from this state immediately after the application is loaded or reset. This vector is also used to set the state dimension of the model. (Linear models)

xhat-filtered state

Filtered state.

xlin-state lin.point

This is the state linearization point of the current linear model.

flin-derived state lin. point

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The state derivative in the linearization point.

yk-measurement

Measurement (output) value. This reflects the latest measurement value read from the input port. This vector is also used to set the output dimension of the model. (Linear models)

yest-est. meas.

Estimated (filtered) output value

ylin-meas. lin.point

This is the output linearization point of the current linear model.

uk-control signal

Control value. This reflects the latest control value read from the input port. This vector is also used to set the control value dimension of the model. (Linear models)

ulin-xontrol lin.point

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This is the control value linearization point of the current linear model.

dk-disturbance

Disturbance. This reflects the latest disturbance value read from the input port. This vector is also used to set the disturbance dimension of the model. (Linear models)

dlin-dist. lin point

This is the disturbance linearization point of the current linear model.

vk-noise

Process noise. This vector is used to set the dimension of the process noise vector.

Bias decision

This vector decides which of the outputs to estimate biases for. When an element in this vector is set to 1, the bias of the corresponding element in the output vector will be estimated.

b0-init. bias

Initial bias value, i.e. the bias from which the filter will start after the application is loaded or reset.

bk-bias

Current bias value.

pk-param est.

Current value for parameter estimates.

ek-pred. error

Prediction error.

Meas. status

Measurement status. If one of the elements of this vector is set to 0, the Kalman filter will not be updated according to the corresponding element of the output vector element. (Ballistic mode)

Parameters

Nonlinear model parameters. This is the configurable parameters of the nonlinear model. The parameter vector is not used for Linear models. The parameter vector has an extra column called Estimate. When an element of this column is set to 1, the corresponding parameter element will be estimated in the Kalman filter. The covariance matrices has to be changed when the choice of estimated parameters is changed.