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基于加强抗差自适应卡尔曼滤波的行人室内定位系统.docx

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基于加强抗差自适应卡尔曼滤波的行人室内定位系统 Title:PedestrianIndoorLocalizationSystemBasedonEnhancedRobustAdaptiveKalmanFiltering Abstract: Theaccurateandreliableindoorlocalizationofpedestriansisofutmostimportanceforvariousapplicationscenarios,suchassmarthomes,indoornavigation,andsecuritysystems.However,indoorenvironmentsposesignificantchallengestotraditionallocalizationmethodsduetothepresenceofmultipathsignals,non-line-of-sight(NLOS)propagation,andlimitedsignalavailability.Toaddressthesechallenges,thispaperproposesapedestrianindoorlocalizationsystembasedonanenhancedrobustadaptiveKalmanfilteringtechnique. Introduction: Indoorlocalizationsystemshavegainedsubstantialattentioninrecentyearsduetotheirpotentialapplicationsinvariousfields.However,achievingaccurateandrobustlocalizationinindoorenvironmentsisacomplextask.TheproposedsystemaimstoovercomethelimitationsofconventionalmethodsbyapplyinganovelapproachbasedonenhancedrobustadaptiveKalmanfiltering. Methodology: 1.SensorFusion: Theproposedsystemintegratesdatafrommultiplesensors,includinginertialmeasurementunits(IMUs)andreceivedsignalstrengthindicator(RSSI)measurementsfromWi-Fiaccesspoints,toimprovetheaccuracyandreliabilityofpedestrianlocalization.ThefusionofsensordataisperformedusingaKalmanfilter,whichprovidesarecursiveestimationofthepedestrian'spositionandvelocity. 2.RobustAdaptiveKalmanFiltering: TomitigatetheeffectsofoutliersandNLOSsignals,anenhancedrobustadaptiveKalmanfilteringtechniqueisemployed.ThismethoddynamicallyadaptsthenoisecovariancematricesoftheKalmanfilterbasedontheinnovationcovariancematrix.Theadaptiveschemeenablesthesystemtoeffectivelyhandletheuncertaintyandnon-Gaussiancharacteristicsofthemeasurements. 3.TrainingPhase: Beforethesystemcanbeusedforlocalization,atrainingphaseisconducted.Duringthisphase,thesystemcollectsdatafromvariousknownlocationswithintheindoorenvironment.ThisdataisusedtoestimatethekeyparametersoftheKalmanfilterandtheadaptivescheme. 4.LocalizationPhase: Oncethesystemistrained,itperformsthelocalizationtaskinrea