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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