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
Version:
$Id: ProcessModel.java 1416643 2012-12-03 19:37:14Z tn $

Method Summary
 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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