《智能传感器装调与测试（双语）》课件-26.联邦卡尔曼滤波

联邦卡尔曼滤波FederatedKalmanFiltering卡尔曼滤波（Kalmanfiltering）是一种利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估计的算法。由于观测数据中包括系统中的噪声和干扰的影响，所以最优估计也可看作是滤波过程。Kalmanfilteringisanalgorithmthatuseslinearsystemstatusequationstooptimallyestimatethesystemstatethroughsysteminputandoutputobservationdata.Sincetheobserveddataincludestheinfluenceofnoiseandinterferenceinthesystem,theoptimalestimatecanalsoberegardedasafilteringprocess.DemandBackground:thePainofCentralizedStructurePART01需求背景：集中式之痛集中式结构的痛点PainPointsofCentralizedStructure▶算力压力大HighpressureoncomputingpowerThecentralizedstructureprocessesallsensordatacentrally,resultinginaheavyburdenonthecomputingpowerofthevehicle-mountedsystemanddifficultyinensuringreal-timeperformance.集中式结构将所有传感器数据集中处理，导致车载系统算力负担沉重，实时性难以保证。▶可靠性差HighpressureoncomputingpowerAsinglesensorfaultmaycausetheentiresystemtoparalyze,seriouslyaffectingthesafetyofsmartvehicles.单一传感器故障可能导致整体系统瘫痪，严重影响智能车的安全性。▶扩展性弱WeakscalabilityWhenaddingnewsensors,theentiremodelneedstobereconstructed.Thedevelopmentcycleislonganditisdifficultforthehardwareplatformtoevolvesmoothly.新增传感器时需重构整个模型，开发周期长，硬件平台难以平滑演进。集中式滤波Centralizedfiltering分散式滤波结构DistributedFilteringStructure随着单一传感器向多传感器状态估计的转变，状态估计的精度得到了显著提升，系统的鲁棒性也得到了增强。分散式方法强调在每个传感器节点本地进行计算和协作，提高了计算效率和系统的容错性。Withthetransitionfromsinglesensortomulti-sensorstatusestimation,theaccuracyofstatusestimationhasbeensignificantlyimprovedandtherobustnessofthesystemhasalsobeenenhanced.Thedistributedapproachemphasizeslocalcomputingandcollaborationateachsensornode,improvingcomputationalefficiencyandsystemfaulttolerance.分散式方法利用了各个传感器的信息，通过融合多个滤波器的输出，获得更加准确和稳定的状态估计结果。Thedistributedmethodusestheinformationofeachsensortoobtainmoreaccurateandstablestatusestimationresultsbyfusingtheoutputsofmultiplefilters.分散式滤波DistributedfilteringPART02核心架构：四大模块CoreArchitecture:FourModules四大模块流程图FlowChartofFourModules模块流程Moduleprocess分散式滤波框架结构流程Distributedfilteringframeworkstructureprocess包括子滤波器、信息分配、主滤波器和信息反馈。

Includessub-filter,informationdistribution,mainfilterandinformationfeedback.模块详解ModuleDetails子滤波器Sub-filter1负责单一传感器数据处理与局部状态估计，如激光雷达处理点云数据，毫米波雷达处理速度信息。

Responsibleforsinglesensordataprocessingandlocalstatusestimation,suchaslaseradarprocessingpointclouddata,millimeter-waveradarprocessingspeedinformation.雷达滤波器Radarfilter传感器性能分配Sensorperformanceallocation模块详解ModuleDetails信息分配Informationdistribution2根据传感器性能分配初始信息权重，激光雷达权重为0.4，毫米波雷达为0.3，摄像头为0.3。Theinitialinformationweightisassignedaccordingtothesensorperformance,withaweightof0.4forlaseradar,0.3formillimeter-waveradarand0.3forcamera.模块详解ModuleDetails主滤波器Mainfilter3融合各子滤波器的局部估计，输出全局最优结果，修正多传感器数据偏差。Thelocalestimatesofsub-filtersarefusedtooutputtheglobaloptimalresultandcorrectthemulti-sensordatadeviation.主滤波器滤波Mainfilterfiltering数据信息反馈Datainformationfeedback模块详解ModuleDetails信息反馈Informationfeedback4将全局最优结果反馈至子滤波器，优化其初始状态，实现闭环优化。Theglobaloptimalresultisfedbacktothesub-filtertooptimizeitsinitialstatusandachieveclosed-loopoptimization.PART03优势与智能车应用场景AdvantagesandApplicationScenariosofIntelligentVehicles三大核心优势ThreeCoreAdvantages可靠性高Highreliability单点故障不影响全局，系统仍可正常运行，满足智能车的安全性需求。Singlepointfaultdoesnotaffecttheoverallsituation,andthesystemcanstilloperatenormallytomeetthesafetyrequirementsofsmartvehicles.算力友好Computing-friendly分散处理降低主滤波器压力，适配车载有限算力，提升系统实时性。Distributedprocessingreducesthepressureonthemainfilter,adaptstothelimitedcomputingpowerofthevehicle,andimprovesthereal-timeperformanceofthesystem.扩展性强Goodscalability新增传感器只需添加子滤波器，无需重构整体模型，实现灵活升级。Newsensorsonlyneedtoaddsub-filterswithoutreconstructingtheoverallmodel,allowingforflexibleupgrades.典型应用场景TypicalApplicationScenarios▶高速自动驾驶High-speedautonomousdrivingIntegratemulti-sensordatatoaccuratelytrackthespeedandpositionofthevehicleinfront,improvingthesafetyofautonomousdriving.融合多传感器数据，精准跟踪前车速度与位置，提升自动驾驶安全性。▶自动泊车AutomaticparkingIntegratecameraandradardatatoaccuratelyidentifyparkinglinesandobstacles,andimprovethesuccessrateofparking.整合摄像头与雷达数据，精准识别车位线及障碍物，提高泊车成功率。典型应用场景TypicalApplicationScenarios▶复杂路况感知PerceptionofComplexRoadConditionsMulti-sensorredundantdesigntocopewithharshenvironmentssuchasrainandfog,ensuringcontinuoustargettracking.多传感器冗余设计，应对雨雾等恶劣环境，保证目标连续跟踪。小结Summary分散式滤波结构是联邦卡尔曼滤波的关键架构，通过“子滤波器（分散处理）、信息分配（权重划分）、主滤波器（集中融合）、信息反馈（闭环优化）”四大模块，实现了多传感器数据的高效协同；它解决了集中式结构的算力、可靠性、扩展性问题，是智能车感知系统的“刚需架构”。ThedistributedfilteringstructureisthekeyarchitectureoffederatedKalmanfiltering.Itrealizesefficientcollaborationofmulti-sensordatathroughfourmodules:sub-filter(distributedprocessing),informationdistribution(weightdivision),mainfilter(centralizedfusion