java.lang.Object

org.apache.commons.math3.filter.DefaultProcessModel

All Implemented Interfaces:: ProcessModel

public class DefaultProcessModel extends Object implements ProcessModel

Default implementation of a ProcessModel for the use with a KalmanFilter.

Since:: 3.0

Constructor Summary

Constructors

Constructor

Description

DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise)

Create a new ProcessModel, taking double arrays as input parameters.

DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance)

Create a new ProcessModel, taking double arrays as input parameters.

DefaultProcessModel(RealMatrix stateTransition, RealMatrix control, RealMatrix processNoise, RealVector initialStateEstimate, RealMatrix initialErrorCovariance)

Create a new ProcessModel, taking double arrays as input parameters.
Method Summary

Modifier and Type

Method

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.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details
- DefaultProcessModel
  
  public DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance) throws NullArgumentException, NoDataException, DimensionMismatchException
  
  Create a new ProcessModel, taking double arrays as input parameters.
  
  Parameters:
  
  stateTransition - the state transition matrix
  
  control - the control matrix
  
  processNoise - the process noise matrix
  
  initialStateEstimate - the initial state estimate vector
  
  initialErrorCovariance - the initial error covariance matrix
  
  Throws:
  
  NullArgumentException - if any of the input arrays is null
  
  NoDataException - if any row / column dimension of the input matrices is zero
  
  DimensionMismatchException - if any of the input matrices is non-rectangular
- DefaultProcessModel
  
  public DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise) throws NullArgumentException, NoDataException, DimensionMismatchException
  
  Create a new ProcessModel, taking double arrays as input parameters.
  The initial state estimate and error covariance are omitted and will be initialized by the KalmanFilter to default values.
  
  Parameters:
  
  stateTransition - the state transition matrix
  
  control - the control matrix
  
  processNoise - the process noise matrix
  
  Throws:
  
  NullArgumentException - if any of the input arrays is null
  
  NoDataException - if any row / column dimension of the input matrices is zero
  
  DimensionMismatchException - if any of the input matrices is non-rectangular
- DefaultProcessModel
  
  public DefaultProcessModel(RealMatrix stateTransition, RealMatrix control, RealMatrix processNoise, RealVector initialStateEstimate, RealMatrix initialErrorCovariance)
  
  Create a new ProcessModel, taking double arrays as input parameters.
  
  Parameters:
  
  stateTransition - the state transition matrix
  
  control - the control matrix
  
  processNoise - the process noise matrix
  
  initialStateEstimate - the initial state estimate vector
  
  initialErrorCovariance - the initial error covariance matrix
Method Details
- getStateTransitionMatrix
  
  public RealMatrix getStateTransitionMatrix()
  
  Returns the state transition matrix.
  
  Specified by:
  
  getStateTransitionMatrix in interface ProcessModel
  
  Returns:
  
  the state transition matrix
- getControlMatrix
  
  public RealMatrix getControlMatrix()
  
  Returns the control matrix.
  
  Specified by:
  
  getControlMatrix in interface ProcessModel
  
  Returns:
  
  the control matrix
- getProcessNoise
  
  public 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.
  Specified by:
  
  getProcessNoise in interface ProcessModel
  
  Returns:
  
  the process noise matrix
  
  See Also:
  
  KalmanFilter.predict()
  
  KalmanFilter.predict(double[])
  
  KalmanFilter.predict(RealVector)
- getInitialStateEstimate
  
  public 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.
  
  Specified by:
  
  getInitialStateEstimate in interface ProcessModel
  
  Returns:
  
  the initial state estimation vector
- getInitialErrorCovariance
  
  public 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.
  
  Specified by:
  
  getInitialErrorCovariance in interface ProcessModel
  
  Returns:
  
  the initial error covariance matrix

Class DefaultProcessModel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Details

DefaultProcessModel

DefaultProcessModel

DefaultProcessModel

Method Details

getStateTransitionMatrix

getControlMatrix

getProcessNoise

getInitialStateEstimate

getInitialErrorCovariance