一种光纤陀螺随机漂移的高精度建模方法-豆柴文库

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一种光纤陀螺随机漂移的高精度建模方法
Title:AHigh-PrecisionModelingMethodforRandomDriftinFiberOpticGyroscopes
Abstract:
Fiberopticgyroscopes(FOGs)arewidelyusedinnavigationalandstabilizationsystemsduetotheirhighaccuracyandcompactsize.However,oneofthemainchallengesinFOGsisthepresenceofrandomdrift,whichcansignificantlyaffecttheaccuracyandreliabilityofthegyro.Inthispaper,weproposeahigh-precisionmodelingmethodtoeffectivelycharacterizetherandomdriftinFOGs.TheproposedmethodcombinesadvancedsignalprocessingtechniquesandmathematicalmodelingtoimprovetheaccuracyandstabilityofFOGs.
1.Introduction
1.1Background
FiberopticgyroscopesarebasedontheSagnaceffectandhavebecomeessentialcomponentsinmanyindustries,includingaerospace,navigation,androbotics.However,theaccuracyofFOGsisinfluencedbyvariousfactors,suchastemperaturevariations,mechanicalvibrations,andpolarizationfluctuations.Amongthesefactors,randomdriftisoneofthemostsignificantchallengesinFOGtechnology.
1.2Motivation
AccuratemodelingofrandomdriftinFOGsiscrucialforimprovingtheperformanceandreliabilityofthesedevices.Traditionalmodelingmethodshavecertainlimitationsintermsofaccuracyandprecision.Therefore,thereisaneedforahigh-precisionmodelingmethodthatcaneffectivelycharacterizetherandomdriftinFOGs.
2.Methodology
2.1DataCollection
TodevelopanaccuratemodelforrandomdriftinFOGs,alargedatasetofreal-worldmeasurementsiscollected.TheFOGissubjectedtovariousenvironmentalconditionsandoperationalscenariostocapturethediverserangeofdriftpatterns.
2.2Preprocessing
Thecollecteddataispreprocessedtoremovenoiseandartifactsthatcouldaffecttheaccuracyofmodeling.Techniquessuchaslow-passfilteringandwaveletdenoisingareappliedtoenhancethesignal-to-noiseratio.
2.3FeatureExtraction
Inthisstep,relevantfeaturesareextractedfromthepreprocesseddata.Featuresmayincludestatisticalmeasuressuchasmean,variance,andskewness,aswellasfrequency-domaincharacteristicssuchaspowerspectraldensityanddominantfrequencies.
2.4MachineLearning-basedModeling
Machinelearningalgorithms,suchassupportvectormachi