gov.sandia.cognition.statistics.bayesian

Class ExtendedKalmanFilter

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<Vector,MultivariateGaussian>

gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter

All Implemented Interfaces:

BatchAndIncrementalLearner<Vector,MultivariateGaussian>, BatchLearner<java.util.Collection<? extends Vector>,MultivariateGaussian>, IncrementalLearner<Vector,MultivariateGaussian>, BayesianEstimator<Vector,Vector,MultivariateGaussian>, RecursiveBayesianEstimator<Vector,Vector,MultivariateGaussian>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="Wikipedia",
                      title="Extended Kalman filter",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Extended_Kalman_filter")
public class ExtendedKalmanFilter
extends AbstractKalmanFilter

Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models. The belief states are still Gaussian and the nonlinear models are approximated using a first-order numerical differentiation approximation. The EKF is not guaranteed to be optimal or converge to the true state.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected class	ExtendedKalmanFilter.ModelJacobianEvaluator Holds the input constant while perturbing the state to estimate the Jacobian (A) matrix

Field Summary

Fields

Modifier and Type	Field and Description
protected StatefulEvaluator<Vector,Vector,Vector>	motionModel Model that determines how inputs and the previous state are updated.
protected Evaluator<Vector,Vector>	observationModel Model that determines how the state is observed.

Fields inherited from class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

currentInput, measurementCovariance, modelCovariance

Constructor Summary

Constructors

Constructor and Description
ExtendedKalmanFilter() Creates a new instance of ExtendedKalmanFilter
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel, Evaluator<Vector,Vector> observationModel, Vector currentInput, Matrix modelCovariance, Matrix measurementCovariance) Creates a new instance of ExtendedKalmanFilter

Method Summary

Modifier and Type	Method and Description
ExtendedKalmanFilter	clone() This makes public the clone method on the Object class and removes the exception that it throws.
MultivariateGaussian	createInitialLearnedObject() Creates a new initial learned object, before any data is given.
StatefulEvaluator<Vector,Vector,Vector>	getMotionModel() Getter for motionModel
Evaluator<Vector,Vector>	getObservationModel() Getter for observationModel
void	measure(MultivariateGaussian belief, Vector observation) Integrates a measurement into the system, refining the current belief of the state of the system
void	predict(MultivariateGaussian belief) Creates a prediction of the system's next state given the current belief state
void	setMotionModel(StatefulEvaluator<Vector,Vector,Vector> motionModel) Setter for motionModel
void	setObservationModel(Evaluator<Vector,Vector> observationModel) Setter for observationModel

Methods inherited from class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter

computeMeasurementBelief, computePredictionCovariance, getCurrentInput, getMeasurementCovariance, getModelCovariance, setCurrentInput, setMeasurementCovariance, setModelCovariance, update

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

learn, learn, update

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner

update

Field Detail

motionModel

protected StatefulEvaluator<Vector,Vector,Vector> motionModel

Model that determines how inputs and the previous state are updated.

observationModel

protected Evaluator<Vector,Vector> observationModel

Model that determines how the state is observed.

Constructor Detail

ExtendedKalmanFilter

public ExtendedKalmanFilter()

Creates a new instance of ExtendedKalmanFilter

ExtendedKalmanFilter

public ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
                            Evaluator<Vector,Vector> observationModel,
                            Vector currentInput,
                            Matrix modelCovariance,
                            Matrix measurementCovariance)

Creates a new instance of ExtendedKalmanFilter

Parameters:

motionModel - Model that determines how inputs and the previous state are updated.

observationModel - Model that determines how the state is observed.

currentInput - Current input to the model.

modelCovariance - Covariance associated with the system's model.

measurementCovariance - Covariance associated with the measurements.

Method Detail

clone

public ExtendedKalmanFilter clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractKalmanFilter

Returns:

A clone of this object.

getMotionModel

public StatefulEvaluator<Vector,Vector,Vector> getMotionModel()

Getter for motionModel

Returns:

Model that determines how inputs and the previous state are updated.

setMotionModel

public void setMotionModel(StatefulEvaluator<Vector,Vector,Vector> motionModel)

Setter for motionModel

Parameters:

motionModel - Model that determines how inputs and the previous state are updated.

getObservationModel

public Evaluator<Vector,Vector> getObservationModel()

Getter for observationModel

Returns:

Model that determines how the state is observed.

setObservationModel

public void setObservationModel(Evaluator<Vector,Vector> observationModel)

Setter for observationModel

Parameters:

observationModel - Model that determines how the state is observed.

createInitialLearnedObject

public MultivariateGaussian createInitialLearnedObject()

Description copied from interface: IncrementalLearner

Creates a new initial learned object, before any data is given.

Returns:

The initial learned object.

predict

public void predict(MultivariateGaussian belief)

Description copied from class: AbstractKalmanFilter

Creates a prediction of the system's next state given the current belief state

Specified by:

predict in class AbstractKalmanFilter

Parameters:

belief - Current belief state

measure

public void measure(MultivariateGaussian belief,
                    Vector observation)

Description copied from class: AbstractKalmanFilter

Integrates a measurement into the system, refining the current belief of the state of the system

Specified by:

measure in class AbstractKalmanFilter

Parameters:

belief - Current belief of the state of the system

observation - Measurement to integrate.