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非高斯环境下的车辆定位与状态估计关键技术（英文版）.docx

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非高斯环境下的车辆定位与状态估计关键技术（英文版） Title:KeyTechniquesforVehicleLocalizationandStateEstimationinNon-GaussianEnvironments Abstract: Modernvehiclesrequireaccurateandrobustlocalizationandstateestimationtechniquestoensuresafeandefficientoperations.TraditionalmethodsbasedonGaussianassumptionsmayfailtoprovidereliableresultsinnon-Gaussianenvironments,characterizedbynonlinearities,multimodaldistributions,anduncertainty.Therefore,thispaperaimstoreviewandanalyzethekeytechniquesthataddressthechallengesassociatedwithvehiclelocalizationandstateestimationinnon-Gaussianenvironments.Thetechniquesdiscussedincludeparticlefilters,unscentedKalmanfilters,Gaussianmixturemodels,andcooperativelocalizationalgorithms.Additionally,thispaperexplorestheintegrationofmultiplesensorsanddatafusiontechniquestoenhancetheaccuracyandreliabilityofvehiclelocalizationandstateestimationinnon-Gaussianenvironments. 1.Introduction 1.1Background 1.2ProblemStatement 1.3Objective 2.Non-GaussianEnvironmentsinVehicleLocalizationandStateEstimation 2.1CharacteristicsofNon-GaussianEnvironments 2.2ChallengesFacedinNon-GaussianEnvironments 3.ParticleFilters 3.1IntroductiontoParticleFilters 3.2ParticleFilteringAlgorithm 3.3AdvantagesandLimitations 3.4ApplicationsinVehicleLocalizationandStateEstimation 4.UnscentedKalmanFilters 4.1IntroductiontoUnscentedKalmanFilters 4.2UnscentedTransform 4.3UnscentedKalmanFilteringAlgorithm 4.4AdvantagesandLimitations 4.5ApplicationsinVehicleLocalizationandStateEstimation 5.GaussianMixtureModelsforVehicleLocalization 5.1IntroductiontoGaussianMixtureModels(GMMs) 5.2GMM-basedLocalizationAlgorithm 5.3AdvantagesandLimitations 5.4ApplicationsinVehicleLocalization 6.CooperativeLocalizationAlgorithms 6.1IntroductiontoCooperativeLocalization 6.2InformationFusionTechniques 6.3CooperativeLocalizationAlgorithms 6.4AdvantagesandLimitations 6.5ApplicationsinVehicleLocalizationandStateEstimation 7.SensorFusionforVehicleLocalizationandStateEstimation 7.1OverviewofSensorFusion 7.2TechniquesforSensorFusion 7.3Mul