GPS动态定位数据处理模型的比较研究 (英文)-豆柴文库

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GPS动态定位数据处理模型的比较研究(英文)
ComparativeResearchonGPSDynamicPositioningDataProcessingModels
Abstract:
GlobalPositioningSystem(GPS)hasbecomeanessentialtechnologyforvariousapplicationssuchasnavigation,location-basedservices,andtracking.GPSdynamicpositioningdataprocessingplaysacrucialroleinextractingaccurateandreliablepositioninformationfromrawGPSmeasurements.Overtheyears,severaldataprocessingmodelshavebeendevelopedandemployedtoenhancetheaccuracyandefficiencyofGPSpositioning.ThispaperaimstoprovideacomparativeanalysisofdifferentGPSdynamicpositioningdataprocessingmodels,highlightingtheirstrengths,weaknesses,andapplicationareas.
1.Introduction:
GPSdynamicpositioningdataprocessinginvolvesseveralstepssuchassignalacquisition,tracking,measurementextraction,andpositionestimation.Differentmodelshavebeenproposedandutilizedtooptimizethesestepsaccordingtospecificrequirements.Thiscomparativeresearchaimstoexploreandanalyzethesemodelstoidentifytheirkeycharacteristicsandapplications.
2.TraditionalKalmanFilteringModel:
ThetraditionalKalmanfilteringmodeliswidelyusedforGPSdynamicpositioningdataprocessing.Itemploysarecursivealgorithmthatestimatesthepositionbasedonpreviousmeasurementsandcalculatestheoptimalestimatebasedonthecurrentmeasurement.Thismodelprovidesaccuratepositioningoutcomesbutmaysufferfromtheaccumulationoferrors,especiallyinscenarioswithsignalblockageormultipatheffects.
3.Extended,Unscented,andParticleFilteringModels:
TotacklethelimitationsofthetraditionalKalmanfilteringmodel,severalextendedversionshavebeenproposed.TheExtendedKalmanFiltering(EKF)modelintroducesalinearizationprocesstohandlenonlinearmeasurementmodels.TheUnscentedKalmanFiltering(UKF)modelusesadeterministicsamplingtechniquetocapturethenonlinearityofthesystem.TheParticleFiltering(PF)modelemploysMonteCarlosimulationstoestimatetheposteriorprobabilitydistributionofthestates.Thesemodelsperformwellinnonlinearandnon-Gaussianscenariosbutmaysufferfromcomputationalcomplexities.
4.RecurrentNeuralNetworkModel:
Withtheadvancementsin