简介:Recentlytherehavebeenresearchesaboutnewefficientnonlinearfilteringtechniques[1]~[3]inwhichthenonlinearfiltersgeneralizeelegantlytononlinearsystemswithouttheburdensomelinearizationsteps.Thus,truncationerrorsduetolinearizationcanbecompensated.ThesefiltersincludetheunscentedKalmanfilter(UKF),thecentraldifferencefilter(CDF)andthedivideddifferencefilter(DDF),andtheyarealsocalledSigmaPointFilters(SPFs)inaunifiedway[4].Forhigherorderapproximationofthenonlinearfunction.ItoandXiong[6]introducedanalgorithmcalledtheGaussHermiteFilter,whichisrevisitedin[5].TheGaussHermiteFiltergivesbetterapproximationattheexpenseofhighercomputationburden,althoughit’slessthantheparticlefilter.TheGaussHermiteFilterisusedasintroducedin[5]withadditionalpruningstepbyaddingthresholdfortheweightstoreducethequadraturepoints.
简介:SeveralfiltertechniqueswereavailablefortheGPSpositionestimationproblemofmaneuveringvehiclerangingfromusingdifferentprocessnoisestoInteractiveMultipleModel(IMM).ThelimitationofusingstandardKalmanfiltersislisted.Theperformanceofproposedadaptivefilteriscomparedwiththatofthestandardones,twotypesofdynamicmodelingofthemaneuveringvehicleareused.ThesimulationisbasedonthealmanacdataoftheGPSsatellitestocomputeitsfeasibilityduringthesimulationtimeandpositiononshape8trackwithcontinuousvehiclemaneuvering.Thegoalistoobtaincomputationallyefficientfilterwithreasonableaccuracyforvehicleinmaneuveringsituation.ThefilterproposedisanalternativetothefilterproposedinRef.[1]withlowcomputationalburden.