《智能传感器装调与测试（双语）》课件-23.INSGNSS 融合算法

INS/GNSS融合算法INS/GNSSFusionAlgorithmINS(惯性导航系统)+GNSS(全球导航卫星系统)INS(InertialNavigationSystem)+GNSS(GlobalNavigationSatelliteSystem)PART01INS和GNSS单独两个系统各自的局限性LimitationsofINSandGNSS融合刚需智能调节器登场IntelligentRegulatorThatMeetsFusionRequirements隧道场景Tunnelscenario-融合需求-Fusionrequirements为解决INS误差累积与GNSS信号依赖的矛盾，卡尔曼滤波被引入。它在GNSS正常时修正INS误差，在GNSS失效时维持精度，确保定位的连续性和可靠性。TosolvethecontradictionbetweenINSerroraccumulationandGNSSsignaldependence,Kalmanfilteringisintroduced.ItcorrectsINSerrorswhenGNSSisnormalandmaintainsprecisionwhenGNSSfails,ensuringpositioningcontinuityandreliability.调节器示意图SchematicdiagramoftheregulatorPART02卡尔曼滤波变量递推Kalmanfilteringvariablerecursion九维误差状态量全景Nine-DimensionalErrorStateQuantityPanorama位置误差Positionerror位置误差包括经度误差、纬度误差和高度误差，反映了INS定位结果与真实位置的偏差。通过卡尔曼滤波估计这些误差，可将INS定位拉回真实位置。Positionerrorsincludelongitudeerrors,latitudeerrors,andaltitudeerrors,whichreflectthedeviationofINSpositioningresultsfromtheactualposition.EstimatingtheseerrorsthroughKalmanfilteringcanbringtheINSpositioningbacktothetrueposition.速度误差Velocityerror速度误差涵盖X轴、Y轴和Z轴的速度误差，用于修正INS速度输出。准确估计速度误差有助于提高运动目标的速度测量精度。VelocityerrorscoverthevelocityerrorsoftheX-axis,Y-axis,andZ-axis,whichareusedtocorrecttheINSvelocityoutput.Accuratelyestimatingvelocityerrorshelpstoimprovethevelocitymeasurementprecisionofmovingtargets.姿态误差

Attitudeerror姿态误差包括横滚角误差、俯仰角误差和航向角误差，这些误差影响INS的姿态测量精度。卡尔曼滤波通过估计姿态误差，优化INS的姿态输出。Attitudeerrorsincluderollangleerrors,pitchangleerrors,andheadingangleerrors,whichaffecttheattitudemeasurementprecisionofINS.KalmanfilteringoptimizestheattitudeoutputofINSbyestimatingattitudeerrors.观测模型ObservationmodelINS运动模型递推INSMotionModelRecursion卡尔曼滤波迭代模型Kalmanfilteringiterativemodel状态量预测Predictionofstatequantity利用INS采集的加速度和角速度数据，结合运动学方程，将上一时刻的误差状态递推到当前时刻，得到状态量预测值X_pred。WiththeaccelerationandangularvelocitydatacollectedbyINS,combinedwiththekinematicequations,theerrorstateatthepreviousmomentisrecursivelyextrapolatedtothecurrentmomenttoobtainthestatequantitypredictionvalueX_pred.状态修正Statecorrection利用GNSS的观测数据对第一步的预测结果进行修正。UseGNSSobservationdatatocorrectthepredictionresultsofthefirststep.PART03模型计算过程Modelcalculationprocess观测残差揭示预测偏离程度ObservationResidualsRevealsTheDegreeofPredictionDeviation将GNSS观测值与INS预测值相减，得到观测残差。残差大小直接反映INS预测的可信度，为计算卡尔曼增益提供输入。TheobservationresidualisthedifferencebetweentheobservedvalueofGNSSandthepredictedvalueofINS.TheresidualsizedirectlyreflectsthecredibilityoftheINSpredictionandprovidesinputforcalculatingtheKalmangain.-观测残差计算-Calculationofobservationresiduals▶1.GNSS测得位置是（1000m,500m）ThepositionmeasuredbyGNSSis(1000m,500m)▶2.INS预测的位置是（1001.2m,500.8m）thepositionpredictedbyINSis(1001.2m,500.8m)▶3.则观测残差为（1000-1001.2,500-500.8）=（-1.2m,-0.8m）andthentheobservationresidualis(1000-1001.2,500-500.8)=(-1.2m,-0.8m)卡尔曼增益动态分配信任权重KalmanGainDynamicallyAllocatesTrustWeight增益K在0到1之间浮动，反映了对GNSS观测的信任程度。当GNSS噪声R增大时，K减小，减少对不可靠观测的依赖；反之，K增大，加强修正力度。ThegainKfluctuatesbetween0and1,reflectingthedegreeoftrustinGNSSobservations.WhentheGNSSnoiseRincreases,Kdecreasestoreducedependenceonunreliableobservations;conversely,Kincreasestostrengthencorrectionefforts.-增益意义-Gainsignificance最优估计更新误差与协方差OptimalEstimateUpdateErrorandCovariance卡尔曼滤波迭代模型Kalmanfilteringiterativemodel最优状态量更新Optimalstatequantityupdate利用卡尔曼增益K对观测残差加权，修正状态量，得到最优状态量残差。修正后的状态误差显著减小。TheobservationresidualisweightedbytheKalmangainK,andthestatequantityiscorrectedtoobtaintheoptimalstatequantityresidual.Thecorrectedstateerrorissignificantlyreduced.最优协方差更新Optimalcovarianceupdate同时更新误差协方差。修正后协方差下降，系统获得更可信的定位结果。Theerrorcovarianceisalsoupdatedatthesametime.Aftercorrection,thecovariancedecreasesandthesystemobtainsmorereliablepositioningresults.PART04影响因素Influencingfactors更新频率与自适应噪声策略UpdateFrequencyandAdaptiveNoiseStrategies实时性Real-time鲁棒性

Robustness卡尔曼滤波的更新频率与GNSS输出频率同步，通常为1-10Hz。高频更新可及时抑制INS误差累积，确保定位的实时性。TheupdatefrequencyofKalmanfilteringissynchronizedwiththeGNSSoutputfrequency,whichusuallyrangesfrom1Hzto10Hz.High-frequencyupdatescansuppresstheaccumulationofINSerrorsintimeandensurereal-timepositioning.当GNSS信号受干扰时，观测噪声方差R自动增大，卡尔曼增益K减小，降低对异常观测的依赖，增强系统的鲁棒性。WhentheGNSSsignalisdisturbed,theobservationnoisevarianceRautomaticallyincreasesandtheKalmangainKdecreases,reducingthedependenceonabnormalobservationsandenhancingtherobustnessofthesystem.小结Summary▶INS/GNSS融合需要借助卡尔曼滤波，以解决INS误差累积与GNSS信号依赖之间的矛盾，其中状态更新是修正误差的关键INS/GNSSfusionrequiresusingKalmanfilteringtosolvethecontradictionbetweenINSerroraccumulationandGNSSsignaldependence.Stateupdatesarekeytocorrectingerrors.核心概念包括Coreconcepts状态量（INS的位置、速度、姿态误差）Statequantity(position,velocity,andattitudeerrorofINS).观测模型（GNSS与INS的残差关联）Observationmodel(residualcorrelationbetweenGNSSandINS)