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基于KLD采样改进的高斯粒子滤波算法（英文）.docx

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基于KLD采样改进的高斯粒子滤波算法（英文） Introduction: Particlefilter(PF)isapowerfultechniqueforstateestimationproblems.Ithasbeenusedinawiderangeofapplications,includingrobotics,computervision,andsignalprocessing.Oneofthemainchallengesofparticlefilteristohandlethecurseofdimensionality,thatis,whenthestatespaceishighdimensional,thenumberofparticlesrequiredtoachieveacceptableestimationaccuracygrowsexponentially.Inrecentyears,severaltechniqueshavebeenproposedtoaddressthisissue,includingKullback-Leiblerdivergence(KLD)samplingandGaussianparticlefilter(GPF).Inthispaper,wepresentanovelapproachthatcombinesthesetwotechniquestoimprovetheperformanceofparticlefilter. KLDSampling: KLDsamplingisatechniquethatusestheKullback-Leiblerdivergencetoadaptivelydeterminethenumberofparticlesrequiredtoachieveadesiredlevelofaccuracy.ThebasicideaistoestimatethevarianceoftheimportanceweightsanduseittocalculatetheKLDbetweenthecurrentdistributionandthetruedistribution.IftheKLDisbelowathreshold,thealgorithmstops,otherwiseitgeneratesnewparticlesuntiltheKLDisbelowthethreshold. GaussianParticleFilter: GaussianparticlefilterisatechniquethatapproximatestheposteriordistributionbyaGaussianmixturemodel.TheparticlesaresampledfromtheGaussianmixturemodel,andtheweightsarecalculatedbyevaluatingthelikelihoodofeachparticle.ThemeanandcovarianceoftheGaussianmixturemodelareupdatedusingaweightedmeanandcovarianceformula. ProposedAlgorithm: TheproposedalgorithmcombinesKLDsamplingandGaussianparticlefiltertoimprovetheperformanceofparticlefilter.Thealgorithmconsistsofthefollowingsteps: 1.Settheinitialstateestimateandcovariance. 2.GeneratetheinitialsetofparticlesfromtheGaussianmixturemodel. 3.Calculatethelikelihoodofeachparticle. 4.CalculatetheimportanceweightsbasedonthelikelihoodandtheKLDsampling. 5.ComputethemeanandcovarianceoftheGaussianmixturemodelusingtheimportanceweights. 6.Resampletheparticlesusingasamplingschemebasedontheimportanceweights. 7.GeneratenewparticlesiftheKLDisaboveathreshold. 8.Repeatsteps3-7untilconvergenceoramaximumnu