Interface ProcessModel
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- All Known Implementing Classes:
DefaultProcessModel
public interface ProcessModel
Defines the process dynamics model for the use with aKalmanFilter.- Since:
- 3.0
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description RealMatrixgetControlMatrix()Returns the control matrix.RealMatrixgetInitialErrorCovariance()Returns the initial error covariance matrix.RealVectorgetInitialStateEstimate()Returns the initial state estimation vector.RealMatrixgetProcessNoise()Returns the process noise matrix.RealMatrixgetStateTransitionMatrix()Returns the state transition matrix.
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Method Detail
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getStateTransitionMatrix
RealMatrix getStateTransitionMatrix()
Returns the state transition matrix.- Returns:
- the state transition matrix
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getControlMatrix
RealMatrix getControlMatrix()
Returns the control matrix.- Returns:
- the control matrix
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getProcessNoise
RealMatrix getProcessNoise()
Returns the process noise matrix. This method is called by theKalmanFilterevery 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)
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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
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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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