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基于改进的UKF算法的室内测距定位.docx

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基于改进的UKF算法的室内测距定位 ImprovingIndoorRangingLocalizationUsinganEnhancedUKFAlgorithm Abstract: Indoorlocalizationplaysacrucialroleinvariousapplications,suchassmarthomes,assettracking,andindoornavigationsystems.Amongthedifferenttechniquesavailable,ranging-basedlocalizationhasgainedsignificantattentionduetoitsaccuracyandeffectiveness.ThispaperproposesanenhancedUnscentedKalmanFilter(UKF)algorithmforindoorranginglocalization,addressingthechallengesofmultipathreflectionsandnon-line-of-sight(NLOS)conditions.TheproposedalgorithmaimstoimprovetheaccuracyandreliabilityofindoorranginglocalizationbymitigatingtheeffectsofmultipathandNLOSerrors.ExperimentalresultsdemonstratethattheenhancedUKFalgorithmachieveshigheraccuracyandrobustnesscomparedtotraditionalranging-basedlocalizationmethods. 1.Introduction: Indoorlocalizationhasbecomeincreasinglyimportantinvariousapplications.TraditionalGPS-basedlocalizationtechniquessufferfromdegradedaccuracyinindoorenvironmentsduetomultipathreflections,signalblockages,andlimitedsatellitevisibility.Incontrast,ranging-basedlocalizationtechniquesutilizewirelesssignals,suchasWi-Fi,Bluetooth,orUltra-Wideband(UWB)signals,toestimatethepositionofthetarget. 2.ChallengesinIndoorRangingLocalization: However,indoorranginglocalizationfacesseveralchallenges.Multipathreflectionsoccurwhenthetransmittedsignalisreflectedbysurroundingobjects,resultinginmultiplesignalpathsreachingthereceiver.Thisleadstoerrorsintime-of-flightestimation,affectingranging-basedlocalizationaccuracy.Inaddition,non-line-of-sight(NLOS)conditions,wherethedirectpathbetweenthetransmitterandreceiverisobstructed,furtherdegradetheaccuracyofrangingmeasurements. 3.TheUnscentedKalmanFilter(UKF)Algorithm: TheUKFalgorithmisanonlinearestimationtechniqueusedforstateestimation.Itprovidessuperioraccuracyinnon-linearsystemscomparedtothetraditionalExtendedKalmanFilter(EKF)algorithm.TheUKFalgorithmincorporatestheUnscentedTransform,adeterministicsamplingmethodtopropagatethestatethroughthenon-linearfunctions.However