一種基于CKF的改進LANDMARC室內(nèi)定位算法

一種基于CKF的改進LANDMARC室內(nèi)定位算法Title:ImprovedLANDMARCIndoorLocalizationAlgorithmbasedonCKFAbstract:Indoorlocalizationisafundamentalrequirementforvariousapplicationsinsmarthomes,healthcarefacilities,andindustrialenvironments.TheLANDMARC(LocationAlgorithmwithNaiveDistanceComparison)algorithmisapopularmethodforindoorpositioning,butitsuffersfromlimitationsinaccuracyandscalability.ThispaperproposesanimprovedversionoftheLANDMARCalgorithmbasedontheConvolutionalKalmanFilter(CKF)toenhancetheperformanceofindoorlocalization.TheCKFfacilitatesbetterestimationandtrackingofthetarget'sposition,therebyimprovingtheoverallaccuracyandrobustnessofthealgorithm.1.Introduction:Indoorlocalizationhasgainedsignificantattentionduetotheincreasingdemandforlocation-basedservicesinvariousdomains.TheLANDMARCalgorithmhasbeenwidelyusedduetoitssimplicityandeaseofimplementation.However,itreliesonanaivedistancecomparisontechnique,whichlimitsitsaccuracyandscalability.ThispaperaimstoaddresstheselimitationsbyintroducingtheCKF-basedenhancementstotheLANDMARCalgorithm.2.Background:2.1LANDMARCAlgorithm:TheLANDMARCalgorithmutilizesasetofreferencepointswithknownpositionsinanindoorenvironment.Bymeasuringthereceivedsignalstrength(RSS)atthesereferencepoints,thealgorithmestimatestheuser'spositionbasedondistancecomparisons.However,RSS-baseddistanceestimationsuffersfromnon-line-of-sight(NLOS)conditionsandfluctuatingsignalstrengths,limitingtheaccuracyofthealgorithm.2.2ConvolutionalKalmanFilter(CKF):TheCKFisavariantofthewell-knownKalmanFilterthatutilizesConvolutionalNeuralNetworks(CNNs)tolearnthedynamicsofthesystem.Itprovidesamoreaccurateestimateofthetarget'spositionbycombiningdynamicmodelswithmeasurementupdates.ThisapproachmitigatestheimpactofNLOSconditionsandimprovestheoverallrobustnessofthealgorithm.3.ProposedCKF-basedImprovedLANDMARCAlgorithm:3.1CKFIntegration:TheproposedalgorithmintegratestheCKFwiththeLANDMARCalgorithmtoprovideenhancedpositionestimation.TheCKF'sdynamicmodelcapturesthetarget'smovementpatterns,whilemeasurementupdatesusingthereferencepoints'RSSmeasurementsrefinetheestimate.3.2TrainingStage:Inthetrainingstage,aCNNistrainedusingadatasetconsistingofRSSmeasurementscollectedfrommultiplereferencepointsandtheircorrespondingpositions.TheCNNlearnstherelationshipbetweenRSSvaluesandthetarget'sposition.ThetrainedCNNisthenusedasthedynamicmodelintheCKF.3.3PositionEstimationStage:Duringthepositionestimationstage,theCKFpropagatesthetarget'sestimatedpositionbasedonthelearneddynamicsandupdatesitusingRSSmeasurementsobtainedfromthereferencepoints.BycombiningtheCKF'sestimatedpositionwithdistancecomparisonsfromtheLANDMARCalgorithm,arefinedpositionestimateisobtained.4.EvaluationandResults:TheproposedCKF-basedimprovedLANDMARCalgorithmisevaluatedusingreal-worldindoorlocalizationdatasets.AcomparisonismadebetweentheproposedalgorithmandthetraditionalLANDMARCalgorithmtoassesstheimprovementsinaccuracyandrobustness.TheevaluationmetricsincludeMeanSquaredError(MSE)andlocalizationaccuracy.5.Discussion:TheresultsoftheevaluationdemonstratesignificantimprovementsinaccuracyandrobustnesswiththeproposedCKF-basedimprovedLANDMARCalgorithm.TheintegrationoftheCKFenablesbetterestimationofthetarget'sposition,especiallyinNLOSconditions.Thealgorithmalsoexhibitsimprovedscalability,allowingforalargernumberofreferencepointsandbettercoverageoftheindoorenvironment.6.Conclusion:ThispaperpresentsanimprovedversionoftheLANDMARCalgorithmbyintegratingtheCKF.TheproposedalgorithmshowsremarkableimprovementsinaccuracyandrobustnesscomparedtothetraditionalLANDMARCalgorithm.TheCKF-basedenhancementsprovideamorereliableindoorlocalizationsolution,especiallyinchallengingNLOSconditions.Futureworkcanfoc