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AttitudeEstimationbasedonExtendedKalmanFilterHanPingHeWeikunMarch9,2011SUPAEROTALBEOFCONTENTSProblemdescriptionExtendedKalmanFilterSystemmodelSystemstructurediagramExperimentresultsConclusionsSomeproblemsintheexperimentProblemdescriptionNavigationistheprocessofcomputingtheposition,orientationandspeedofairplane.Oneoftheimportanttasksoftheinertialnavigationsystemsistointegratetheoutputofaccelerometersandmagneticmeterstoobtainvelocityandattitudeofairplane,whichisnormallyaccomplishedbyKalmanFilter.Nowadays,navigationtechniquebasedonKalmanFilterhasbeenwellusedinmanyproducts,suchasCHR-6dmAHRSsystemandSBGsystem.TheCHR-6dmAHRSisacost-effectiveorientationsensorprovidingyaw,pitch,androllangleoutputsatupto300Hz.AnExtendedKalmanFilter(EKF)combinesdatafromonboardaccelerometers,rategyros,andmagneticsensorstoproduceyaw,pitch,androllangleestimates.SBGsystemoffersacompletelineofminiatureandhighperformanceinertialsystems,3Dcompass,inclinometersandaccelerometers.Itsperformance,reliabilityandeaseofusehavebeenkepttothehighestlevels,butitismoreexpensivecomparedwithCHRsystem.ThepriceofCHRsystemismorethan$100(about60€),whileSBGsystemismorethan1000€.OurtaskinthisprojectistoimprovetheperformanceofCHRsystemtoinsteadSBGsystemusedinthelabplane.InCHRsystem,wecanobtainthefollowingparametersfromtheratesensors,accelerometersandmagnetometersrespectively:angularrates(CHR_gyro)gravityrates(CHR_acc)localmagneticfieldvector(CHR_mag)Astheabovedatasetsareverynoisy,weneedtodesignagoodKalmanFiltertoestimatetheplane’sattitude(pitch,roll,yaw,angularrate).ExtendedKalmanFilterKalmanfiltercomputesthesolutionrecursively.Eachupdatedestimateofthestateiscomputedfromthepreviousestimationandthenewinputdata.Foranonlinearsystem,theKalmanfiltermodelis(1)where,xissystemstatevector,udenotesinput,yisoutput,wandvarenoisecorrespondingtothestateequationandoutputequationrespectively.meansnonlineartransformfunctionfromthecurrentsystemstatetothenextstate.presentsano
