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java.lang.Objectde.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractAssociationDistribution<S,T>
de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl.AssociationDistribution<S,T>
S
- Type of discrete variables in the multi target observationT
- Type of discrete variables in the multi target statepublic class AssociationDistribution<S extends TargetID,T extends TargetID>
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!!
Field Summary | |
---|---|
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 | |
---|---|
AssociationDistribution(java.util.Random rand,
AbstractMultiState<S> Z,
AbstractMultiObservationDistributionIndep<S,T> observationDistrib,
LogProbabilityDensityFunction spatialClutterDistrib,
LogProbabilityDensityFunction spatialNewbornDistrib,
LogProbabilityMassFunction mu,
LogProbabilityMassFunction nu,
double P_D)
Constructor. |
|
AssociationDistribution(java.util.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 | |
---|---|
DataAssociation |
drawSample()
Generate a new sample from this density. |
DataAssociation |
drawSampleDebug(DataAssociation groundtruth,
java.io.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 |
---|
protected double[][] chi
protected int lastM
protected int lastN
protected DataAssociation lastSample
protected double[] logBinom
protected double[] logMuValues
protected double[] logNuValues
protected double logP_C
protected double logP_MN
protected int M_max
protected int minMN
protected LogProbabilityMassFunction mu
protected int newtargetID
protected LogProbabilityMassFunction nu
protected double P_D
protected double[] phi_0
protected double[] phi_1
protected double[] psi
Constructor Detail |
---|
public AssociationDistribution(java.util.Random rand, AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib, LogProbabilityDensityFunction spatialClutterDistrib, LogProbabilityDensityFunction spatialNewbornDistrib, LogProbabilityMassFunction mu, LogProbabilityMassFunction nu, double P_D)
rand
- random generator for samplingZ
- the current observationsobservationDistrib
- distribution of the observations modelspatialClutterDistrib
- spatial distribution of possible clutter appearancespatialNewbornDistrib
- spatial distribution of possible newborn appearancemu
- distribution of the number of clutter observationsnu
- distribution of the number of observations from newborn targetsP_D
- probability of target detectionpublic AssociationDistribution(java.util.Random rand, AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib, LogProbabilityDensityFunction spatialClutterDistrib, LogProbabilityDensityFunction spatialNewbornDistrib, LogProbabilityMassFunction mu, LogProbabilityMassFunction nu, double P_D, int M_max)
rand
- random generator for samplingZ
- the current observationsobservationDistrib
- distribution of the observations modelspatialClutterDistrib
- spatial distribution of possible clutter appearancespatialNewbornDistrib
- spatial distribution of possible newborn appearancemu
- distribution of the number of clutter observationsnu
- distribution of the number of observations from newborn targetsP_D
- probability of target detectionM_max
- maximum number of observations in the time seriesMethod Detail |
---|
public DataAssociation drawSample()
SamplingDistribution
drawSample
in interface SamplingDistribution<DataAssociation>
drawSample
in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>
public DataAssociation drawSampleDebug(DataAssociation groundtruth, java.io.OutputStream ostream)
drawSampleDebug
in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>
public void letNewbornTargetIDsStartFrom(int minNewTargetID)
public double log_p(DataAssociation x)
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 !!
log_p
in interface LogEvaluatableDistribution<DataAssociation>
x
- realization of random variable X
public double p(DataAssociation x)
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 !!
p
in interface EvaluatableDistribution<DataAssociation>
x
- realization of random variable X
protected void reset()
public void setNewObservations(AbstractMultiState<S> Z, AbstractMultiObservationDistributionIndep<S,T> observationDistrib)
setNewObservations
in class AbstractAssociationDistribution<S extends TargetID,T extends TargetID>
|
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