de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl

Class AssociationDistribution<S extends TargetID,T extends TargetID>

java.lang.Object

de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractAssociationDistribution<S,T>

de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl.AssociationDistribution<S,T>

Type Parameters:

S - Type of discrete variables in the multi target observation

T - Type of discrete variables in the multi target state

All Implemented Interfaces:

EvaluatableDistribution<DataAssociation>, LogEvaluatableDistribution<DataAssociation>, SamplingDistribution<DataAssociation>

Direct Known Subclasses:

AssociationDistributionNN

public class AssociationDistribution<S extends TargetID,T extends TargetID>
extends AbstractAssociationDistribution<S,T>
implements EvaluatableDistribution<DataAssociation>, LogEvaluatableDistribution<DataAssociation>

Association distribution to sample association variables for a set of observations based on a model of how observations are formed.

Observations are comprised from existing targets that are detected with probability P_D, a number of observations of newborn targets distributed according to a distribution nu and a number of clutter observations distributed according to a distribution mu.

The association variables of the individual observations are sampled sequentially and their distributions are assumed to depend on the likelihood of the current observation for a specific realization of the association variable and the probability of the association variable given all previous associations. Note that the likelihood of observations that are not yet associated is not considered here!!

Author:

Oliver Gress

Field Summary

Fields

Modifier and Type	Field and Description
protected double[][]	chi
protected int	lastM Number of observations in last call of drawSample()
protected int	lastN Number of targets in last call of drawSample()
protected DataAssociation	lastSample Last sample that was sampled
protected double[]	logBinom Binomial distribution of number of observations associated to existing targets
protected double[]	logMuValues (log) values of mu to avoid recomputation
protected double[]	logNuValues (log) values of nu to avoid recomputation
protected double	logP_C (log) probability of the current set of association variables given observations and previous associations
protected double	logP_MN (log) propability of M observations given N existing targets and the current model configuration
protected int	M_max Maximum number of observations in the time series
protected int	minMN Minimum of number of observations and number of targets
protected LogProbabilityMassFunction	mu Distribution of the number of clutter observations
protected int	newtargetID Target-ID to start from for newborn targets
protected LogProbabilityMassFunction	nu Distribution of the number of observations from newborn targets
protected double	P_D Probability of target detection
protected double[]	phi_0
protected double[]	phi_1
protected double[]	psi

Fields inherited from class de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractAssociationDistribution

assocfactory, clutterdistrib, log_pzc, M, N, newborndistrib, obsdistrib, rand, Z

Constructor Summary

Constructors

Constructor and Description
AssociationDistribution(Random rand, AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib, LogProbabilityDensityFunction spatialClutterDistrib, LogProbabilityDensityFunction spatialNewbornDistrib, LogProbabilityMassFunction mu, LogProbabilityMassFunction nu, double P_D) Constructor.
AssociationDistribution(Random rand, AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib, LogProbabilityDensityFunction spatialClutterDistrib, LogProbabilityDensityFunction spatialNewbornDistrib, LogProbabilityMassFunction mu, LogProbabilityMassFunction nu, double P_D, int M_max) Constructor where the maximum number of observations in the time series is specified to avoid some re-computations.

Method Summary

Modifier and Type	Method and Description
DataAssociation	drawSample() Generate a new sample from this density.
DataAssociation	drawSampleDebug(DataAssociation groundtruth, OutputStream ostream)
void	letNewbornTargetIDsStartFrom(int minNewTargetID) Specify the starting target-ID for newborn targets
double	log_p(DataAssociation x) This method is here only valid for the latest DataAssociation sampled with drawSample().
double	p(DataAssociation x) This method is here only valid for the latest DataAssociation sampled with drawSample().
protected void	reset()
void	setNewObservations(AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib)

Methods inherited from class java.lang.Object

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

Field Detail

mu

protected LogProbabilityMassFunction mu

Distribution of the number of clutter observations

logMuValues

protected double[] logMuValues

(log) values of mu to avoid recomputation

nu

protected LogProbabilityMassFunction nu

Distribution of the number of observations from newborn targets

logNuValues

protected double[] logNuValues

(log) values of nu to avoid recomputation

logBinom

protected double[] logBinom

Binomial distribution of number of observations associated to existing targets

lastM

protected int lastM

Number of observations in last call of drawSample()

lastN

protected int lastN

Number of targets in last call of drawSample()

psi

protected double[] psi

phi_0

protected double[] phi_0

phi_1

protected double[] phi_1

chi

protected double[][] chi

minMN

protected int minMN

Minimum of number of observations and number of targets

P_D

protected double P_D

Probability of target detection

logP_MN

protected double logP_MN

(log) propability of M observations given N existing targets and the current model configuration

lastSample

protected DataAssociation lastSample

Last sample that was sampled

logP_C

protected double logP_C

(log) probability of the current set of association variables given observations and previous associations

M_max

protected int M_max

Maximum number of observations in the time series

newtargetID

protected int newtargetID

Target-ID to start from for newborn targets

Constructor Detail

AssociationDistribution

public AssociationDistribution(Random rand,
                               AbstractMultiState<S> Z,
                               AbstractMultiObservationDistributionIndep<S,T> observationDistrib,
                               LogProbabilityDensityFunction spatialClutterDistrib,
                               LogProbabilityDensityFunction spatialNewbornDistrib,
                               LogProbabilityMassFunction mu,
                               LogProbabilityMassFunction nu,
                               double P_D)

Constructor.

Parameters:

rand - random generator for sampling

Z - the current observations

observationDistrib - distribution of the observations model

spatialClutterDistrib - spatial distribution of possible clutter appearance

spatialNewbornDistrib - spatial distribution of possible newborn appearance

mu - distribution of the number of clutter observations

nu - distribution of the number of observations from newborn targets

P_D - probability of target detection

AssociationDistribution

public AssociationDistribution(Random rand,
                               AbstractMultiState<S> Z,
                               AbstractMultiObservationDistributionIndep<S,T> observationDistrib,
                               LogProbabilityDensityFunction spatialClutterDistrib,
                               LogProbabilityDensityFunction spatialNewbornDistrib,
                               LogProbabilityMassFunction mu,
                               LogProbabilityMassFunction nu,
                               double P_D,
                               int M_max)

Constructor where the maximum number of observations in the time series is specified to avoid some re-computations.

Parameters:

rand - random generator for sampling

Z - the current observations

observationDistrib - distribution of the observations model

spatialClutterDistrib - spatial distribution of possible clutter appearance

spatialNewbornDistrib - spatial distribution of possible newborn appearance

mu - distribution of the number of clutter observations

nu - distribution of the number of observations from newborn targets

P_D - probability of target detection

M_max - maximum number of observations in the time series

Method Detail

letNewbornTargetIDsStartFrom

public void letNewbornTargetIDsStartFrom(int minNewTargetID)

Specify the starting target-ID for newborn targets

drawSample

public DataAssociation drawSample()

Description copied from interface: SamplingDistribution

Generate a new sample from this density. This method should create a new object.

Specified by:

drawSample in interface SamplingDistribution<DataAssociation>

Specified by:

drawSample in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>

Returns:

new sample object

drawSampleDebug

public DataAssociation drawSampleDebug(DataAssociation groundtruth,
                                       OutputStream ostream)

Specified by:

drawSampleDebug in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>

p

public double p(DataAssociation x)

This method is here only valid for the latest DataAssociation sampled with drawSample(). If no DataAssociation was sampled before or the given DataAssociation x is a different object than the latest sampled DataAssociation or a new observation was set, this method return -1 !!

Specified by:

p in interface EvaluatableDistribution<DataAssociation>

Parameters:

x - realization of random variable X

Returns:

value of p(X) at x

log_p

public double log_p(DataAssociation x)

This method is here only valid for the latest DataAssociation sampled with drawSample(). If no DataAssociation was sampled before or the given DataAssociation x is a different object than the latest sampled DataAssociation or a new observation was set, this method returns Double.NaN !!

Specified by:

log_p in interface LogEvaluatableDistribution<DataAssociation>

Parameters:

x - realization of random variable X

Returns:

value of log(p(X)) at x

setNewObservations

public void setNewObservations(AbstractMultiState<S> Z,
                               AbstractMultiObservationDistributionIndep<S,T> observationDistrib)

Overrides:

setNewObservations in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>

reset

protected void reset()