org.apache.commons.math3.filter

## Interface ProcessModel

- All Known Implementing Classes:
    DefaultProcessModel

      public interface ProcessModel

  Defines the process dynamics model for the use with a `KalmanFilter`.
  Since:
    3.0

- - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `RealMatrix` | `getControlMatrix()` Returns the control matrix.
    `RealMatrix` | `getInitialErrorCovariance()` Returns the initial error covariance matrix.
    `RealVector` | `getInitialStateEstimate()` Returns the initial state estimation vector.
    `RealMatrix` | `getProcessNoise()` Returns the process noise matrix.
    `RealMatrix` | `getStateTransitionMatrix()` Returns the state transition matrix.

- - ### Method Detail

    - #### getStateTransitionMatrix

          RealMatrix getStateTransitionMatrix()

      Returns the state transition matrix.
      Returns:
        the state transition matrix

    - #### getControlMatrix

          RealMatrix getControlMatrix()

      Returns the control matrix.
      Returns:
        the control matrix

    - #### getProcessNoise

          RealMatrix getProcessNoise()

      Returns the process noise matrix. This method is called by the `KalmanFilter` every prediction step, so implementations of this interface may return a modified process noise depending on the current iteration step.
      Returns:
        the process noise matrix
      See Also:
        `KalmanFilter.predict()`, `KalmanFilter.predict(double[])`, `KalmanFilter.predict(RealVector)`

    - #### getInitialStateEstimate

          RealVector getInitialStateEstimate()

      Returns the initial state estimation vector.

      Note: if the return value is zero, the Kalman filter will initialize the state estimation with a zero vector.

      Returns:
        the initial state estimation vector

    - #### getInitialErrorCovariance

          RealMatrix getInitialErrorCovariance()

      Returns the initial error covariance matrix.

      Note: if the return value is zero, the Kalman filter will initialize the error covariance with the process noise matrix.

      Returns:
        the initial error covariance matrix

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