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1、UnscentedFilteringandNonlinearEstimationSIMONJ.JULIER,MEMBER,IEEE,ANDJEFFREYK.UHLMANN,MEMBER,IEEEInvitedPaperTheextendedKalmanfilter(EKF)isprobablythemostwidelymostareeithercomputationallyunmanageableorrequireusedestimationalgorithmfornonlinearsystems.However,morespecialassumptionsabouttheformofthep
2、rocessandthan35yearsofexperienceintheestimationcommunityhasshownobservationmodelsthatcannotbesatisfiedinpractice.Forthatisdifficulttoimplement,difficulttotune,andonlyreliableforsystemsthatarealmostlinearonthetimescaleoftheupdates.Manytheseandotherreasons,theKFremainsthemostwidelyofthesedifficultiesa
3、risefromitsuseoflinearization.Toovercomeusedestimationalgorithm.thislimitation,theunscentedtransformation(UT)wasdevelopedasTheKFonlyutilizesthefirsttwomomentsofthestateamethodtopropagatemeanandcovarianceinformationthrough(meanandcovariance)initsupdaterule.Althoughthisisanonlineartransformations.Itis
4、moreaccurate,easiertoimplement,relativelysimplestaterepresentation,itoffersanumberofandusesthesameorderofcalculationsaslinearization.Thispaperreviewsthemotivation,development,use,andimplicationsoftheimportantpracticalbenefits.UT.1)ThemeanandcovarianceofanunknowndistributionKeywords—Estimation,Kalman
5、filtering,nonlinearsystems,requiresthemaintenanceofonlyasmallandconstanttargettracking.amountofinformation,butthatinformationissuffi-cienttosupportmostkindsofoperationalactivities(e.g.,definingavalidationgateforasearchregionforI.INTRODUCTIONatarget).Thus,itisasuccessfulcompromisebetweenThispapercons
6、iderstheproblemofapplyingtheKalmancomputationalcomplexityandrepresentationalflexi-filter(KF)tononlinearsystems.Estimationinnonlinearbility.Bycontrast,thecompletecharacterizationofansystemsisextremelyimportantbecausealmostallpracticalevolvingerrordistributionrequiresthemaintenanceofsystems—fromtarget
7、tracking[1]tovehiclenavigation,anunboundednumberofparameters.Evenifitwerefromchemicalprocessplantcontrol[2]todialysisma-possibletomaintaincompletepdfinformation,thatin-chines—involvenonlinearitiesofon
