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1、ASequentialMonteCarloMethodforParticleFiltersHongzhiGaoandRichardGreenDepartmentofComputerScienceandSoftwareEngineering,UniversityofCanterbury,ChristchurchNewZealand.{honghzi.gao,richard.green}@canterbury.ac.nzAbstractAnobjectorientedparticlefilterframeworkisproposedba
2、sedonsequentialMonteCarlomethods.Particlefilterisanextensivelyusedalgorithmforvisionbasedtrackingsystems.However,littleworkhasbeendoneinthepastliteraturetoinvestigatetheimplementationstrategiesoftheparticlefilteralgorithm.Inthispaper,weproposeaframeworkbasedonopensourc
3、eparticlefilterlibrariesandevaluaterespectiveadvantagesanddisadvantages.Theresultssupporttheproposedobjectorientedparticlefilterbeingamostusefultoolforcomputervisionbasedstochasticprediction.Keywords:particlefilter,implementationstrategy,applicationframework1.Introduct
4、ionThispaperwillinvestigatetheimplementationstrategiesoftheparticlefilterandquantitativelyParticlefilterisanonparametricalternativetoevaluatehowtheseimplementationissuesaffectitsGaussianbasedtechniques,suchasKalmanfilter[1],trackingaccuracy.whichprovidesatractableimple
5、mentationoftheBayesfilter[2]indecomposedstatespace.Insteadofrelyingonafixedfunctionalformoftheposterior,theInthenextsection,wediscusssomeopensourcekeyideaoftheparticlefilteristoapproximatetheparticlefilterimplementations.Insectionthree,theposteriorbelief???????byafinit
6、enumberofsamples,overallstructureoftheparticlefilterisintroduced,whicharerandomlydrawnfromthisposteriorfollowedbythedetailsofourimplementation.In?????????sectionfour,weevaluateourimplementationofthedistributionanddenotedasΧ?????,??,……,??particlefilterquantitativelyandd
7、iscusshowthe[3].accuracyandefficiencyoftheparticlefilterareaffectedbyimplementationdecisions,suchaschoosingofparameters.WewillconcludethispaperComparingwithparametricbasedBayesfilterinsectionfive.implementations,particlefilterhasfollowingadvantages[3]:Firstly,particlef
8、ilterhasan‘a(chǎn)ny-time’[3]characteristic,whichenablesdesignerstotradeoffaccuracywithcomputationalefficiency.Secondly,2.R
