一種INS輔助的PPP周跳探測方法Title:INS-AssistedPPPCycleSlipDetectionMethodAbstract:PrecisePointPositioning(PPP)isawidelyusedtechniqueforhigh-precisionpositioningapplications,relyingondual-frequencycarrierphasemeasurementsfromGlobalNavigationSatelliteSystems(GNSS).However,thepresenceofcycleslipsinthecarrierphasemeasurementscansignificantlydegradethepositioningaccuracy.ThispaperpresentsanovelmethodtodetectcycleslipsinPPPusingInertialNavigationSystem(INS)measurementsasanauxiliarysourceofinformation.TheproposedmethodcombinestheadvantagesofbothGNSSandINStoimprovereal-timecycleslipdetectionaccuracy.1.Introduction1.1Background1.2Motivation1.3Objectives2.LiteratureReview2.1PPPCycleSlipDetection2.2INSIntegrationwithPPP2.3OtherApproachesforCycleSlipDetection3.Methodology3.1OverviewoftheProposedMethod3.2INSDataCollectionandCalibration3.3INSDataFusionwithPPPObservations3.4CycleSlipDetectionAlgorithm3.5Real-TimeImplementationConsiderations4.ExperimentalSetup4.1TestEnvironment4.2DataCollection4.3DataAnalysis5.ResultsandDiscussion5.1PerformanceEvaluationMetrics5.2ComparisonwithExistingMethods5.3DiscussionofFindings6.Conclusion6.1SummaryofContributions6.2PracticalImplications6.3FutureResearchDirections1.Introduction1.1BackgroundPrecisePointPositioning(PPP)isaGNSS-basedtechniquethataimstoachievehigh-precisionpositioningbyestimatingreceiverclockbiases,ionosphericandtroposphericdelays,andsatelliteorbits.However,theaccuracyofPPPissusceptibletovariouserrorsources,oneofwhichiscycleslipsincarrierphasemeasurements.Cycleslipsoccurwhenthephasemeasurementsexperienceanabruptchangeofanintegermultipleofthecarrierwavelength.Detectingandcorrectingcycleslipspromptlyiscrucialtomaintainaccuratepositioning.1.2MotivationConventionalmethodsforcycleslipdetectioninPPPpredominantlyutilizestatisticalpropertiesofGNSSobservables.However,thesemethodsmaybepronetofalsedetectionsandcanfailtoidentifyallcycleslips,especiallyinchallengingenvironments.Therefore,incorporatingadditionalinformationfromINSmeasurementscanenhancecycleslipdetectionaccuracyandimprovetherobustnessofPPP.1.3ObjectivesThemainobjectiveofthisstudyistoproposeanINS-assistedcycleslipdetectionmethodforPPPthatexploitsthecomplementarypropertiesofGNSSandINS.Theproposedmethodaimstoimprovetheaccuracyandreal-timeperformanceofcycleslipdetection,leadingtoenhancedpositioningaccuracyandreliability.2.LiteratureReview2.1PPPCycleSlipDetectionExistingcycleslipdetectionmethodsforPPPincluderatiotests,LAMBDAmethods,powerspectraldensityanalysis,andKalmanfilter-basedtechniques.ThesemethodsprimarilyrelyonstatisticalpropertiesoftheGNSSobservablesandhavelimitationsindetectingcycleslipsaccuratelyandinreal-time.2.2INSIntegrationwithPPPIntegrationofINSwithPPPhasbeenextensivelystudiedintheliterature,mainlyfocusingonaidingPPPinitializationandenhancingpositioningaccuracy.However,limitedresearchhasexploredtheuseofINSmeasurementsforcycleslipdetectioninPPP.2.3OtherApproachesforCycleSlipDetectionAlternativeapproachesforcycleslipdetectionincludetheuseofcarrier-smoothedcodemeasurements,dual-frequencymeasurements,andGNSSreceiverinternaldata.ThesemethodshaveshownimprovementoverconventionalmethodsbutcanbenefitfromtheintegrationofINSmeasurements.3.Methodology3.1OverviewoftheProposedMethodTheproposedmethodutilizesINSmeasurementstoenhancethedetectionofcycleslipsinPPP.INSmeasurementsprovideinformationabouttheuser'smotionanddynamics,whichcanaidinidentifyingplausiblecycleslipevents.3.2INSDataCollectionandCalibrationINSmeasurementsarecollectedusingahigh-precisioninertialmeasurementunit(IMU)integratedwiththeGNSSreceiver.ThecollecteddataarecalibratedandsynchronizedwithGNSSobservationsforfurtherprocessing.3.3INSDataFusionwithPPPObservationsTheINSmeasurementsareintegratedwithPPPobservablesinatightlycouplednavigationfilter,leveragingthecomplementaryinformationfrombothsystems.Theintegrationprocessimprovesthequalityofthepositionsolutionandprovidesadditionalcontextualinformationforcycleslipdetection.3.4CycleSlipDetectionAlgorithmTheproposedcycleslipdetectionalgorithmutilizesacombinationofstatisticaltestsanddynamicbehavioranalysis.ThestatisticaltestsanalyzetheresidualsbetweenthepredictedandobservedGNSScarrierphasemeasurements,whilethedynamicbehavioranalysisexaminestheconsistencybetweentheuser'sposition,velocity,andaccelerationprofilesderivedfromINSmeasurements.3.5Real-TimeImplementationConsiderationsReal-timeimplementationoftheproposedmethodrequiresefficientdataprocessingalgorithms,low-latencyINSdatafusiontechniques,andanoptimizedcycleslipdetectionalgorithm.Theseconsiderationsarediscussedindetailtoensurethepracticalapplicabilityoftheproposedmethod.4.ExperimentalSetup4.1TestEnvironmentAcomprehensivetestingenvironmentisestablished,includingbothopen-skyscenariosandchallengingurbanenvironmentswithpotentialmultipathandsignalblockage.MultipleGNSSreceiversandanINS-equippedplatformareusedtocollectdataundervariousconditions.4.2DataCollectionDatacollectionisperformedforasignificantduration,capturingdiversemotionpatternsandGNSSsignalcharacteristics.Multiplereferencestationsareusedfordifferentialcorrectionandpreciseorbitdeterminationofthesatellites.4.3DataAnalysisThecollecteddataareanalyzedtoevaluatetheaccuracyandeffectivenessoftheproposedINS-assistedcycleslipdetectionmethod.Performancemetricssuchasdetectionrate,falsedetectionrate,andpositioningaccuracyarecomputedandcomparedwithexistingcycleslipdetectionmethods.5.ResultsandDiscussion5.1PerformanceEvaluationMetricsTheproposedmethodisevaluatedbasedondetectionrate,falsedetectionrate,andpositioningaccuracymetrics.Thesemetricsprovideinsightsintotheeffectivenessofthemethodinaccuratelydetectingcycleslipsandimprovingtheoverallpositioningperformance.5.2ComparisonwithExistingMethodsTheresultsobtainedfromtheproposedmethodarecomparedwithconventionalandexistingcycleslipdetectionmethods.ThecomparativeanalysisdemonstratesthesuperiorityandefficacyoftheproposedINS-assistedmethod.5.3DiscussionofFindingsThefindingsfromtheexperimentalevaluationarediscussedindetail,highlightingthestrengths,limitations,andpotentialareasforimprovementoftheproposedmethod.Recommendationsforfutureresearchdirectionsarealsoprovided.6.Conclusion6.1SummaryofContributionsTheproposedINS-assistedcycleslipdetectionmethodforPPPcombinestheadvantagesofGNSSandINStoimprovereal-timecycleslipdetectionaccuracy.ThemethodleveragesthecomplementaryinformationfrombothsystemsandenhancesthereliabilityandaccuracyofPPPpositioning.6.2PracticalImplicationsTheproposedmethodhasseveralpracticalimplicationsforhigh-precisionpositioningapplications,suchasautonomousvehicles,robotics,andsurveying.Theimprovedcycleslipdetectionaccuracycanl