Apache Commons Math 3.6.1 · org.apache.commons.math3.filter.DefaultProcessModel

Field Summary

Modifier and Type	Field and Description
private RealMatrix	controlMatrix The control matrix, used to integrate a control input into the state estimation.
private RealMatrix	initialErrorCovMatrix The initial error covariance matrix of the observed process.
private RealVector	initialStateEstimateVector The initial state estimation of the observed process.
private RealMatrix	processNoiseCovMatrix The process noise covariance matrix.
private RealMatrix	stateTransitionMatrix The state transition matrix, used to advance the internal state estimation each time-step.

Constructor Summary

Access	Constructor and Description
public	DefaultProcessModel(final double[][] the state transition matrix stateTransition, final double[][] the control matrix control, final double[][] the process noise matrix processNoise, final double[] the initial state estimate vector initialStateEstimate, final double[][] the initial error covariance matrix initialErrorCovariance) Create a new `ProcessModel`, taking double arrays as input parameters.
public	DefaultProcessModel(final double[][] the state transition matrix stateTransition, final double[][] the control matrix control, final double[][] the process noise matrix processNoise) Create a new `ProcessModel`, taking double arrays as input parameters.
public	DefaultProcessModel(final RealMatrix the state transition matrix stateTransition, final RealMatrix the control matrix control, final RealMatrix the process noise matrix processNoise, final RealVector the initial state estimate vector initialStateEstimate, final RealMatrix the initial error covariance matrix initialErrorCovariance) Create a new `ProcessModel`, taking double arrays as input parameters.

Method Summary

Modifier and Type	Method and Description
public RealMatrix	getControlMatrix() Implements org.apache.commons.math3.filter.ProcessModel.getControlMatrix. Returns the control matrix.
public RealMatrix	getInitialErrorCovariance() Implements org.apache.commons.math3.filter.ProcessModel.getInitialErrorCovariance. Returns the initial error covariance matrix.
public RealVector	getInitialStateEstimate() Implements org.apache.commons.math3.filter.ProcessModel.getInitialStateEstimate. Returns the initial state estimation vector.
public RealMatrix	getProcessNoise() Implements org.apache.commons.math3.filter.ProcessModel.getProcessNoise. Returns the process noise matrix.
public RealMatrix	getStateTransitionMatrix() Implements org.apache.commons.math3.filter.ProcessModel.getStateTransitionMatrix. Returns the state transition matrix.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

controlMatrix	back to summary
private RealMatrix controlMatrix The control matrix, used to integrate a control input into the state estimation.

initialErrorCovMatrix	back to summary
private RealMatrix initialErrorCovMatrix The initial error covariance matrix of the observed process.

initialStateEstimateVector	back to summary
private RealVector initialStateEstimateVector The initial state estimation of the observed process.

processNoiseCovMatrix	back to summary
private RealMatrix processNoiseCovMatrix The process noise covariance matrix.

stateTransitionMatrix	back to summary
private RealMatrix stateTransitionMatrix The state transition matrix, used to advance the internal state estimation each time-step.

Constructor Detail

DefaultProcessModel back to summary

DefaultProcessModel	back to summary
public DefaultProcessModel(final double[][] stateTransition, final double[][] control, final double[][] processNoise, final double[] initialStateEstimate, final double[][] initialErrorCovariance) throws NullArgumentException, NoDataException, DimensionMismatchException Create a new `ProcessModel`, taking double arrays as input parameters. Parameters stateTransition:double[][] the state transition matrix control:double[][] the control matrix processNoise:double[][] the process noise matrix initialStateEstimate:double[] the initial state estimate vector initialErrorCovariance:double[][] the initial error covariance matrix Exceptions 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

public DefaultProcessModel(final double[][] stateTransition, final double[][] control, final double[][] processNoise, final double[] initialStateEstimate, final double[][] initialErrorCovariance) throws NullArgumentException, NoDataException, DimensionMismatchException

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

Parameters

stateTransition:double[][]: the state transition matrix
control:double[][]: the control matrix
processNoise:double[][]: the process noise matrix
initialStateEstimate:double[]: the initial state estimate vector
initialErrorCovariance:double[][]: the initial error covariance matrix

Exceptions

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 back to summary

DefaultProcessModel	back to summary
public DefaultProcessModel(final double[][] stateTransition, final double[][] control, final 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:double[][] the state transition matrix control:double[][] the control matrix processNoise:double[][] the process noise matrix Exceptions 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

public DefaultProcessModel(final double[][] stateTransition, final double[][] control, final 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:double[][]: the state transition matrix
control:double[][]: the control matrix
processNoise:double[][]: the process noise matrix

Exceptions

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 back to summary

DefaultProcessModel	back to summary
public DefaultProcessModel(final RealMatrix stateTransition, final RealMatrix control, final RealMatrix processNoise, final RealVector initialStateEstimate, final RealMatrix initialErrorCovariance) Create a new `ProcessModel`, taking double arrays as input parameters. Parameters stateTransition:RealMatrix the state transition matrix control:RealMatrix the control matrix processNoise:RealMatrix the process noise matrix initialStateEstimate:RealVector the initial state estimate vector initialErrorCovariance:RealMatrix the initial error covariance matrix

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

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

Parameters

stateTransition:RealMatrix: the state transition matrix
control:RealMatrix: the control matrix
processNoise:RealMatrix: the process noise matrix
initialStateEstimate:RealVector: the initial state estimate vector
initialErrorCovariance:RealMatrix: the initial error covariance matrix

Method Detail

getControlMatrix	back to summary
public RealMatrix getControlMatrix() Implements org.apache.commons.math3.filter.ProcessModel.getControlMatrix. Doc from org.apache.commons.math3.filter.ProcessModel.getControlMatrix. Returns the control matrix. Returns:RealMatrix the control matrix

getControlMatrix

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public RealMatrix getControlMatrix()

Implements org.apache.commons.math3.filter.ProcessModel.getControlMatrix.

Doc from org.apache.commons.math3.filter.ProcessModel.getControlMatrix.

Returns the control matrix.

Returns:RealMatrix: the control matrix

getInitialErrorCovariance	back to summary
public RealMatrix getInitialErrorCovariance() Implements org.apache.commons.math3.filter.ProcessModel.getInitialErrorCovariance. Doc from org.apache.commons.math3.filter.ProcessModel.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:RealMatrix the initial error covariance matrix

getInitialErrorCovariance

back to summary

public RealMatrix getInitialErrorCovariance()

Implements org.apache.commons.math3.filter.ProcessModel.getInitialErrorCovariance.

Doc from org.apache.commons.math3.filter.ProcessModel.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:RealMatrix: the initial error covariance matrix

getInitialStateEstimate	back to summary
public RealVector getInitialStateEstimate() Implements org.apache.commons.math3.filter.ProcessModel.getInitialStateEstimate. Doc from org.apache.commons.math3.filter.ProcessModel.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:RealVector the initial state estimation vector

getInitialStateEstimate

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public RealVector getInitialStateEstimate()

Implements org.apache.commons.math3.filter.ProcessModel.getInitialStateEstimate.

Doc from org.apache.commons.math3.filter.ProcessModel.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:RealVector: the initial state estimation vector

getProcessNoise back to summary

getProcessNoise	back to summary
public RealMatrix getProcessNoise() Implements org.apache.commons.math3.filter.ProcessModel.getProcessNoise. Doc from org.apache.commons.math3.filter.ProcessModel.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:RealMatrix the process noise matrix

public RealMatrix getProcessNoise()

Implements org.apache.commons.math3.filter.ProcessModel.getProcessNoise.

Doc from org.apache.commons.math3.filter.ProcessModel.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:RealMatrix: the process noise matrix

getStateTransitionMatrix	back to summary
public RealMatrix getStateTransitionMatrix() Implements org.apache.commons.math3.filter.ProcessModel.getStateTransitionMatrix. Doc from org.apache.commons.math3.filter.ProcessModel.getStateTransitionMatrix. Returns the state transition matrix. Returns:RealMatrix the state transition matrix

getStateTransitionMatrix

back to summary

public RealMatrix getStateTransitionMatrix()

Implements org.apache.commons.math3.filter.ProcessModel.getStateTransitionMatrix.

Doc from org.apache.commons.math3.filter.ProcessModel.getStateTransitionMatrix.

Returns the state transition matrix.

Returns:RealMatrix: the state transition matrix

public Class DefaultProcessModel

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