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基于DE蝙蝠算法优化粒子滤波的目标跟踪.docx

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基于DE蝙蝠算法优化粒子滤波的目标跟踪 Title:OptimizationofParticleFilteringforObjectTrackingUsingDifferentialEvolutionBatAlgorithm Abstract: Objecttrackingisafundamentaltaskincomputervisionandhasnumerousapplicationsinfieldssuchassurveillance,robotics,andautonomousvehicles.Particlefilteringisapopulartrackingmethodthatestimatesthestateofanobjectbyusingasetofparticles.However,theparticlefilter'sperformanceheavilyreliesontheaccuracyandefficiencyofitsoptimizationalgorithm.ThispaperproposestheintegrationoftheDifferentialEvolutionBatAlgorithm(DEBA)withparticlefilteringtoenhancethetrackingaccuracyandefficiency. Introduction: Objecttrackingisessentialformonitoringandanalyzingmovingobjectsinvariousapplications.Particlefilteringisawidely-usedtechniqueforobjecttrackingduetoitsabilitytohandlenon-linearandnon-Gaussiandistributions.However,traditionalparticlefilteringalgorithmsoftenfacechallengesintrackingefficientlyandaccurately,especiallyincomplexscenarios.Toaddresstheselimitations,thispaperintroducestheintegrationoftheDEBAwithparticlefilteringtoimprovethetrackingperformance. LiteratureReview: Theparticlefilteralgorithmhasbeenwidelyexploredandappliedinobjecttracking.TraditionalalgorithmslikeSequentialImportanceResampling(SIR)sufferfromthedegeneracyproblem,wheremostoftheparticlesbecomeirrelevantafterresampling.Toovercomethisissue,strategiessuchasresample-moves,Rao-Blackwellization,andauxiliaryparticlefiltershavebeenproposed.However,thesemethodsoftenrequireadditionalcomplexstepsandcomputations,reducingtheoveralltrackingperformance.Inrecentyears,metaheuristicalgorithmshavegainedpopularityduetotheirglobalsearchcapabilitiesandabilitytooptimizecomplexproblems.TheBatAlgorithm(BA)andtheDifferentialEvolution(DE)algorithmhaveshownpromisingresultsinvariousoptimizationproblems.Combiningthestrengthsofthesetwoalgorithmscanpotentiallyenhancetheperformanceofparticlefilteringforobjecttracking. Methodology: TheproposedapproachintegratestheDEBAwithparticlefilteringforimprovedobjecttracking.First,aninitialsetofparti