Uses of Class
de.unihalle.informatik.MiToBo.math.LinearTransformGaussNoise

Packages that use LinearTransformGaussNoise
de.unihalle.informatik.MiToBo.tracking.multitarget.algo   
de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl   
 

Uses of LinearTransformGaussNoise in de.unihalle.informatik.MiToBo.tracking.multitarget.algo
 

Fields in de.unihalle.informatik.MiToBo.tracking.multitarget.algo declared as LinearTransformGaussNoise
protected  LinearTransformGaussNoise[] MultiTargetIMMFilter.predictors
          dynamic models
protected  LinearTransformGaussNoise MultiTargetIMMFilter.projector
          observation model
 

Methods in de.unihalle.informatik.MiToBo.tracking.multitarget.algo that return LinearTransformGaussNoise
protected  LinearTransformGaussNoise[] MultiObservationGenerator.createDynamicModels()
           
protected  LinearTransformGaussNoise MultiObservationGenerator.createObservationModel()
           
 

Methods in de.unihalle.informatik.MiToBo.tracking.multitarget.algo with parameters of type LinearTransformGaussNoise
protected  MultiState<MotionModelID> MultiObservationGenerator.generateNextStates(MultiState<MotionModelID> X, LinearTransformGaussNoise[] dynamicModels)
           
protected  MultiState<MotionModelID> MultiObservationGenerator.generateObservations(MultiState<MotionModelID> states, LinearTransformGaussNoise obsModel, MultiStateFactory<MotionModelID> obsFactory)
           
 

Constructors in de.unihalle.informatik.MiToBo.tracking.multitarget.algo with parameters of type LinearTransformGaussNoise
MultiTargetIMMFilter(Vector<GaussMixDistribution> initialStateDistrib, LinearTransformGaussNoise observationModel, LinearTransformGaussNoise[] dynamicsModels, Jama.Matrix markov, double delta_t, ExponentialDistribution targetDeathDistrib, GaussMixDistribution newbornStateDistrib, Jama.Matrix stateFromObs, AbstractMultiStateFactory<MotionModelID> factoryX, AbstractMultiStateFactory<MotionModelID> factoryZ)
          Constructor that initializes the internal random generator with seed 1.
MultiTargetIMMFilter(Vector<GaussMixDistribution> initialStateDistrib, LinearTransformGaussNoise observationModel, LinearTransformGaussNoise[] dynamicsModels, Jama.Matrix markov, double delta_t, ExponentialDistribution targetDeathDistrib, GaussMixDistribution newbornStateDistrib, Jama.Matrix stateFromObs, AbstractMultiStateFactory<MotionModelID> factoryX, AbstractMultiStateFactory<MotionModelID> factoryZ)
          Constructor that initializes the internal random generator with seed 1.
MultiTargetIMMFilter(Vector<GaussMixDistribution> initialStateDistrib, LinearTransformGaussNoise observationModel, LinearTransformGaussNoise[] dynamicsModels, Jama.Matrix markov, double delta_t, ExponentialDistribution targetDeathDistrib, GaussMixDistribution newbornStateDistrib, Jama.Matrix stateFromObs, AbstractMultiStateFactory<MotionModelID> factoryX, AbstractMultiStateFactory<MotionModelID> factoryZ, Random rand)
          Constructor.
MultiTargetIMMFilter(Vector<GaussMixDistribution> initialStateDistrib, LinearTransformGaussNoise observationModel, LinearTransformGaussNoise[] dynamicsModels, Jama.Matrix markov, double delta_t, ExponentialDistribution targetDeathDistrib, GaussMixDistribution newbornStateDistrib, Jama.Matrix stateFromObs, AbstractMultiStateFactory<MotionModelID> factoryX, AbstractMultiStateFactory<MotionModelID> factoryZ, Random rand)
          Constructor.
 

Uses of LinearTransformGaussNoise in de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl
 

Methods in de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl with parameters of type LinearTransformGaussNoise
 void MultiStateDistributionIndepGaussians.predict(LinearTransformGaussNoise predictor)
           
 void MultiStateDistributionIndepGaussians.predictIndep(int i, LinearTransformGaussNoise predictor)
           
 void MultiStateDistributionIndepGaussians.update(LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
           
 void MultiStateDistributionIndepGaussians.updateIndep(int i, int j, LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
          Update i-th Gaussian component with j-th observation
 void MultiStateDistributionIndepGaussians.updateIndep(int i, LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
           
 



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