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基于卡尔曼滤波的动态权值融合.docx

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基于卡尔曼滤波的动态权值融合 Title:DynamicWeightedFusionBasedonKalmanFilteringAlgorithm Abstract: Inrecentyears,fusionalgorithmshavegainedsignificantimportanceinvariousfields,includingcomputervision,sensornetworks,androbotics.ThispaperfocusesonthedynamicweightedfusionmethodbasedontheKalmanfilteringalgorithm.Thedynamicweightedfusionapproachaimstoimprovetheaccuracyandrobustnessofdatafusionbyadaptivelyadjustingtheweightsassignedtoeachsensorordatasourcebasedontheirperformanceandreliability. 1.Introduction Datafusion,alsoknownassensorfusionorinformationfusion,involvesintegratinginformationfrommultiplesourcestoobtainamoreaccurateandcompleteunderstandingofaparticularphenomenon.Traditionalfusionalgorithmsoftenassignfixedweightstoeachsource,assumingthatsourcesareequallyreliableorhaveknownpriorcharacteristics.However,theseassumptionsmaynotholdtrueinmanyreal-worldscenarios,leadingtosuboptimalfusionresults.Therefore,adynamicweightedfusionmethodisrequiredtoadjusttheweightsasthereliabilityandcharacteristicsofeachsourcechangeovertime. 2.KalmanFilteringAlgorithm TheKalmanfilteringalgorithmisawidelyusedtechniqueforstateestimationindynamicsystems.ItisbasedontheprinciplesofrecursiveestimationandBayesianinference,allowingfortheestimationofsystemstatesfromnoisyandincompletemeasurements.Thealgorithmmaintainsanestimateofthecurrentstateandupdatesititerativelyasnewmeasurementsarrive.Kalmanfilterequationsinvolvetwomainsteps:predictionandupdate.Thepredictionstepusesthesystemdynamicsmodeltoestimatethenextstate,whiletheupdatestepcorrectsthepredictionbasedonthemeasurementsobtained. 3.DynamicWeightedFusionFramework Theproposeddynamicweightedfusionframeworkconsistsofthreemaincomponents:sensor/modelfusion,weightupdate,andfusedoutputgeneration.Inthesensor/modelfusionstep,themeasurementsfromdifferentsourcesarecombinedusingtheKalmanfilteringalgorithm.Theweightupdatestepadjuststheweightsassignedtoeachsourcebasedontheirperformanceandreliability.Finally,thefusedoutputgenerationstepcomputestheweightedaverageofthefilter