gov.sandia.cognition.statistics.bayesian
Class KalmanFilter

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<Vector,MultivariateGaussian>
          extended by gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
              extended by gov.sandia.cognition.statistics.bayesian.KalmanFilter
All Implemented Interfaces:
BatchAndIncrementalLearner<Vector,MultivariateGaussian>, BatchLearner<Collection<? extends Vector>,MultivariateGaussian>, IncrementalLearner<Vector,MultivariateGaussian>, BayesianEstimator<Vector,Vector,MultivariateGaussian>, RecursiveBayesianEstimator<Vector,Vector,MultivariateGaussian>, CloneableSerializable, Serializable, Cloneable

@PublicationReferences(references=@PublicationReference(author="Wikipedia",title="Kalman filter",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Kalman_filter"))
public class KalmanFilter
extends AbstractKalmanFilter

A Kalman filter estimates the state of a dynamical system corrupted with white Gaussian noise with observations that are corrupted with white Gaussian noise.

Since:
3.0
Author:
Kevin R. Dixon
See Also:
Serialized Form

Field Summary
static int DEFAULT_DIMENSION
          Default autonomous dimension, 1.
protected  LinearDynamicalSystem model
          Motion model of the underlying system.
 
Fields inherited from class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter
currentInput, measurementCovariance, modelCovariance
 
Constructor Summary
KalmanFilter()
          Creates a new instance of KalmanFilter
KalmanFilter(int dim)
          Creates an autonomous, fully observable linear dynamical system with the given dimensionality
KalmanFilter(LinearDynamicalSystem model, Matrix modelCovariance, Matrix measurementCovariance)
          Creates a new instance of LinearUpdater
 
Method Summary
 KalmanFilter 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.
 LinearDynamicalSystem getModel()
          Getter for model
 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 setModel(LinearDynamicalSystem model)
          Setter for model
 
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

DEFAULT_DIMENSION

public static final int DEFAULT_DIMENSION
Default autonomous dimension, 1.

See Also:
Constant Field Values

model

protected LinearDynamicalSystem model
Motion model of the underlying system.

Constructor Detail

KalmanFilter

public KalmanFilter()
Creates a new instance of KalmanFilter


KalmanFilter

public KalmanFilter(int dim)
Creates an autonomous, fully observable linear dynamical system with the given dimensionality

Parameters:
dim - Dimensionality of the LDS

KalmanFilter

public KalmanFilter(LinearDynamicalSystem model,
                    Matrix modelCovariance,
                    Matrix measurementCovariance)
Creates a new instance of LinearUpdater

Parameters:
model - Motion model of the underlying system.
modelCovariance - Covariance associated with the system's model.
measurementCovariance - Covariance associated with the measurements.
Method Detail

clone

public KalmanFilter 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.

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.

getModel

public LinearDynamicalSystem getModel()
Getter for model

Returns:
Motion model of the underlying system.

setModel

public void setModel(LinearDynamicalSystem model)
Setter for model

Parameters:
model - Motion model of the underlying system.

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.