gov.sandia.cognition.statistics.bayesian

Class SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<ObservationType,DataDistribution<ParameterType>>

gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter<ObservationType,ParameterType>

gov.sandia.cognition.statistics.bayesian.SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType>

Type Parameters:

ObservationType - Type of observations handled by the algorithm.

ParameterType - Type of parameters to infer.

All Implemented Interfaces:

BatchAndIncrementalLearner<ObservationType,DataDistribution<ParameterType>>, BatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>, IncrementalLearner<ObservationType,DataDistribution<ParameterType>>, BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>, ParticleFilter<ObservationType,ParameterType>, RecursiveBayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author={"M. Sanjeev Arulampalam","Simon Maskell","Neil Gordon","Tim Clapp"},
                      title="A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking",
                      type=Journal,
                      publication="IEEE Transactions on Signal Processing, Vol. 50, No. 2",
                      year=2002,
                      pages={174,188},
                      url="http://people.cs.ubc.ca/~murphyk/Software/Kalman/ParticleFilterTutorial.pdf")
public class SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType>
extends AbstractParticleFilter<ObservationType,ParameterType>

An implementation of the standard Sampling Importance Resampling particle filter.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface gov.sandia.cognition.statistics.bayesian.ParticleFilter

ParticleFilter.Updater<ObservationType,ParameterType>

Field Summary

Fields

Modifier and Type	Field and Description
protected double	particlePctThreadhold Percentage of effective particles, below which we resample.

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

numParticles, random, updater

Constructor Summary

Constructors

Constructor and Description
SamplingImportanceResamplingParticleFilter() Creates a new instance of SamplingImportanceResamplingParticleFilter

Method Summary

Modifier and Type	Method and Description
double	getParticlePctThreadhold() Getter for particlePctThreadhold
void	setParticlePctThreadhold(double particlePctThreadhold) Setter for particlePctThreadhold
void	update(DataDistribution<ParameterType> particles, ObservationType value) The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

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

clone, computeEffectiveParticles, createInitialLearnedObject, getNumParticles, getRandom, getUpdater, setNumParticles, setRandom, setUpdater

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

particlePctThreadhold

protected double particlePctThreadhold

Percentage of effective particles, below which we resample.

Constructor Detail

SamplingImportanceResamplingParticleFilter

public SamplingImportanceResamplingParticleFilter()

Creates a new instance of SamplingImportanceResamplingParticleFilter

Method Detail

getParticlePctThreadhold

public double getParticlePctThreadhold()

Getter for particlePctThreadhold

Returns:

Number of effective particles, below which we resample.

setParticlePctThreadhold

public void setParticlePctThreadhold(double particlePctThreadhold)

Setter for particlePctThreadhold

Parameters:

particlePctThreadhold - Number of effective particles, below which we resample.

update

public void update(DataDistribution<ParameterType> particles,
                   ObservationType value)

Description copied from interface: IncrementalLearner

The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Parameters:

particles - The object to update.

value - The new data for the learning algorithm to use to update the object.