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基于扩展卡尔曼滤波的时差定位算法.docx

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基于扩展卡尔曼滤波的时差定位算法 Abstract Timedifferenceofarrival(TDOA)positioningisanimportanttechniqueforlocalizationinwirelesssensornetworks.Inthispaper,aTDOApositioningalgorithmbasedonextendedKalmanfilter(EKF)isproposed.ThealgorithmutilizestheTDOAmeasurementsobtainedfrommultiplereferencenodestoestimatethepositionofthetargetnode. TheEKFalgorithmisarecursiveestimationalgorithmthatiswidelyusedinavarietyofapplications,includingsensorfusion,navigation,andtracking.Itsmainadvantageistheabilitytohandlenonlinearsystemsbylinearizingthesystemdynamicsandmeasurementequations.Intheproposedalgorithm,thenonlinearmotionmodelandmeasurementequationsoftheTDOApositioningsystemarelinearizedusingafirst-orderTaylorexpansion.ThisallowstheEKFtoestimatethetargetnodepositionwithhighaccuracyandefficiency. TheTDOAmeasurementsareobtainedbymeasuringthetimedifferencebetweenthearrivalofthesignalateachreferencenode.Thesemeasurementsarethenusedtocalculatethehyperbolicequations,whichdescribethepossiblelocationsofthetargetnode.Bycombiningthehyperbolicequationsandthelinearizedmotionmodel,theEKFestimatesthepositionofthetargetnodeiteratively,takingintoaccounttheuncertaintiesinthemeasurementsandthesystemdynamics. Simulationresultsdemonstratetheeffectivenessoftheproposedalgorithminaccuratelyestimatingthepositionofthetargetnode.ThealgorithmoutperformstraditionalTDOApositioningalgorithmsintermsofaccuracyandconvergencespeed.Itisalsorobusttomeasurementerrorsandnoise,providingreliablepositioningresultseveninthepresenceofuncertainties. Inconclusion,theproposedTDOApositioningalgorithmbasedonEKFprovidesanefficientandaccuratesolutionforlocalizationinwirelesssensornetworks.ThealgorithmcombinesTDOAmeasurements,hyperbolicequations,andtheEKFestimationtoestimatethepositionofthetargetnodewithhighprecision.Furtherresearchcanbedonetoinvestigatethealgorithm'sperformanceinreal-worldscenariosanditspotentialforapplicationinvariousindustriessuchasindoornavigation,assettracking,andemergencyresponsesystems. Keywords:Timedifferenceofarrival(TDO