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基于能量有效的水下分布式粒子滤波跟踪算法.docx

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基于能量有效的水下分布式粒子滤波跟踪算法 Introduction: Underwatertrackingisacrucialtechnologyinvariousapplications,suchasoceanographicresearch,marinebiology,underwatercommunication,andautonomousunderwatervehicles(AUVs).Theparticlefilteralgorithmisoneofthemostpopularapproachesforunderwatertrackingduetoitsrobustnessandflexibility.However,theconventionalparticlefilteralgorithmhasseverallimitationsinunderwaterenvironments,suchasthehighdimensionalityofthestatespace,nonlinearmotionmodel,andmeasurementnoise. Toovercometheselimitations,researchershaveproposeddifferentmethods,includingthedistributedparticlefilteralgorithm.Unliketheconventionalparticlefilteralgorithm,thedistributedparticlefilteralgorithmdividesthestatespaceintoseveralsmallersubspacesandperformstheparticlefilteringalgorithmindependentlyineachsubspace.Thisapproachreducesthecomputationalcomplexityandimprovesthetrackingperformanceinunderwaterenvironments.However,thedistributedparticlefilteralgorithmstillhaslimitations,suchasthecommunicationcostandenergyconsumption. Inthispaper,weproposeanenergy-efficientunderwaterdistributedparticlefiltertrackingalgorithmthatreducesthecommunicationoverheadandenergyconsumptionwhilemaintainingthetrackingperformance. Methodology: Theproposedalgorithmcomprisestwomaincomponents:thedistributedparticlefilteralgorithmandtheenergy-efficientstrategy.Thedistributedparticlefilteralgorithmdividesthestatespaceintoseveralsubspacesandperformstheparticlefilteringalgorithmindependentlyineachsubspace.Theenergy-efficientstrategyreducesthecommunicationoverheadandenergyconsumptionbyoptimizingthesamplingstrategyandparticleweightupdate. Thesamplingstrategyoptimizesthesamplesizeandthedistributionoftheparticlesineachsubspace.Weusetheadaptivesamplingtechniquetoadjustthesamplesizeanddistributionbasedonthetrackingperformance.Thistechniquereducesthenumberofparticlesrequiredtoachievehighaccuracyandimprovestheenergyefficiencyofthealgorithm. Theparticleweightupdatestrategyoptimizestheweightupdateprocessbasedonthemeasurementnoiseand