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1、粒子滤波及其在无线通信中的应用AbstractOptimalbayesianestimationiscommonlyusedtocalculatetheposteriorprobabilitydensitydistributionofthetargetstate,inlineargaussiansystem,themeanandvarianceofstatecanbeobtainedbyrecursivekalmanfiltering,however,fornonlineardynamicsystems,wecan’tgettheaccurateana
2、lyticalsolutionsofposteriordistributionduetothepresenceofmultipleintegral,sotherearemanyapproximatesuboptimalalgorithmisproposedtosolvethisproblem,intheseoptimizationalgorithms,particlefilterhasthebestestimationprecisionandhasthebestdevelopmentprospects.Asthemainmethodsofstatees
3、timationinthenonlineardynamicsystem,particlefilterisakindofthroughthenonparametricofthemontecarlosimulationmethodtosolvetherecursivebayesianfilter,itisusingasetofparticlesofthestatespacetoupdateaccordingtobayesruletoestimatetheposterioriprobabilitydensityoftheunknownstates,whent
4、hesampleparticlestendstoinfinity,thentheprobabilitydensitywhichisapproximatedcanbeequivalenttothetrueposteriorprobabilitydensity.FortheStateestimationofnonlinearsystem,particularlynonlinearnon-gaussiansystems,particlefilterhasprovidedasimpleandeffectivetools,ithasreceivedmoreand
5、moreattentionforitssimpledesign,programmingandeasyimplementation,ithasbeenwidelyusedincommunications,radar,navigation,targettrackingandmachinelearning,andotherfields.Althoughparticlefilterhasgotmanygratifyingprogressbothathomeandabroad,it’sdevelopmenttimeisnotlongandhasmanykeyte
6、chnicalproblemsarestillunresolved,suchashowtobuildtheproposaldistributionfunction,thesampledepletionwhichiscausedbyresamplingalgorithm,weightsdegradationphenomenon,thealgorithmconvergenceproblems.ThisarticlestartwiththestudyoftheoptimalBayesianfiltermethod,fromlineargaussfilteri
7、ngmethodgradualtransitiontotheparticlefilteralgorithm,mainlystudiestheproposedistributionfunctionandresamplingtechnique,andthentwokindsofimprovedparticlefilteralgorithmisproposed.Oneofthemisfromtheperspectiveofdifferentialevolutionalgorithm,byintroducingtheadaptivestrategy,manag
8、ingthesesamplewithawayofadaptivevariationandada