org.apache.commons.math.filter
Class DefaultProcessModel

java.lang.Object
  org.apache.commons.math.filter.DefaultProcessModel

All Implemented Interfaces:

ProcessModel

public class DefaultProcessModel
extends java.lang.Object
implements ProcessModel

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

Version:

$Id$

Constructor Summary

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

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 Detail

DefaultProcessModel

public DefaultProcessModel(double[][] stateTransition,
                           double[][] control,
                           double[][] processNoise,
                           double[] initialStateEstimate,
                           double[][] 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

DefaultProcessModel

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

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

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 Detail

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 predict step, so implementations of this interface may return a modified process noise depending on 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