一種GEO軌道導(dǎo)航衛(wèi)星位置保持期間自主導(dǎo)航方法

一種GEO軌道導(dǎo)航衛(wèi)星位置保持期間自主導(dǎo)航方法

Abstract:

ThispaperpresentsanautonomousnavigationmethodforGEOorbitnavigationsatelliteduringposition-keeping.Themethodutilizesthemeasurementsfrommultiplesensorstoestimatethesatellite’spositionandvelocity,andutilizessensorfusiontechniquestoimprovetheaccuracyofthepositionandvelocityestimates.ToreducethecomputationburdenoftheKalmanfilter,anadaptivefilteringapproachisproposed.Thesimulationresultsshowthattheproposedmethodcanachievehighnavigationaccuracyandrobustnessduringposition-keeping.

Introduction:

Asthedemandforsatellite-basednavigationservicescontinuestogrow,itbecomesincreasinglyimportanttomaintaintheaccuracyandreliabilityofsatellitenavigationsystems.InthecaseofGEOorbitnavigationsatellites,theposition-keepingtaskiscrucialformaintainingthestabilityandcontinuityofthesatellitenavigationsystem.

Duringtheposition-keepingtask,thenavigationsatelliteneedstomaintainitspositionwithrespecttoaparticularpointontheearthsurface.Thetraditionalmethodusesthegroundcontrolsystemtoadjustthesatellite'sattitudeandorbittomaintainitsposition.However,thismethodrequiresconstantcommunicationbetweenthesatelliteandthegroundstation,whichcanleadtocommunicationlagandlatencyissuesincertainsituations.

Inrecentyears,researchinteresthasshiftedtowardsdevelopinganautonomousnavigationmethodforposition-keepingofGEOorbitnavigationsatellites.Theautonomousnavigationmethodusesonboardsensorstoestimatethesatellite'spositionandvelocity,andcontrolthesatellite'sattitudeandorbittomaintainitsposition.

Inthispaper,weproposeanautonomousnavigationmethodforGEOorbitnavigationsatelliteposition-keeping.Theproposedmethodutilizesthemeasurementsfrommultiplesensorstoestimatethesatellite'spositionandvelocity,andutilizessensorfusiontechniquestoimprovetheaccuracyofthepositionandvelocityestimates.ToreducethecomputationburdenoftheKalmanfilter,anadaptivefilteringapproachisproposed.

Methodology:

SensorConfiguration:

ThesensorconfigurationforautonomousnavigationofGEOorbitnavigationsatelliteposition-keepingincludesaGNSSreceiver,aninertialmeasurementunit(IMU),andastarsensor.TheGNSSreceiverprovidesthepositionandvelocityinformation,theIMUprovidestheattitudeandangularrateinformation,andthestarsensorprovidestheattitudeinformation.

EstimationAlgorithm:

Thenavigationstateofthesatellitecanberepresentedasx=[r,v,q],whereristhepositionvector,visthevelocityvector,andqisthequaternionrepresentingtheattitudeofthesatellite.Themeasurementvectorcanberepresentedasz=[zGNSS,zIMU,zstar],wherezGNSSistheGNSSmeasurement,zIMUistheIMUmeasurement,andzstaristhestarsensormeasurement.

ThenavigationstateestimationisperformedusingaKalmanfilter.ThemeasurementmodelandstatetransitionmodelfortheKalmanfilteraregivenby:

z=Hx+v

x(k)=Fx(k-1)+w(k-1)

whereHisthemeasurementmatrix,Fisthestatetransitionmatrix,vandwarethemeasurementnoiseandprocessnoiserespectively.

Toimprovetheaccuracyofthepositionandvelocityestimation,sensorfusiontechniquesareemployed.ThesensorfusionalgorithmusesacomplementaryfiltertocombinetheGNSSandIMUdata,andanextendedKalmanfiltertofusethestarsensordatawiththeGNSS/IMUdata.

AdaptiveFiltering:

ThecomputationburdenoftheKalmanfilterincreasesasthenumberofmeasurementsandsensornoiseincrease.Toreducethecomputationburden,anadaptivefilteringapproachisproposed.Theadaptivefilterselectsthemeasurementswithhighinformationcontentanddiscardsthemeasurementswithlowinformationcontent.

Theadaptivefilterusestheinformationgainmetrictoevaluatetheinformationcontentofameasurement.Theinformationgainmetricisdefinedas:

g(t)=det(P(t|t-1))/det(P(t|t))

whereP(t|t-1)istheaprioriestimatecovariancematrixandP(t|t)istheaposterioriestimatecovariancematrix.

TheadaptivefilterdiscardsthemeasurementswithlowinformationgainandselectsthemeasurementswithhighinformationgainforprocessingbytheKalmanfilter.

SimulationResults:

TheproposedautonomousnavigationmethodwassimulatedusingtheMatlab/Simulinksimulationenvironment.Thesimulationresultsshowedthattheproposedmethodcanachievehighnavigationaccuracyandrobustnessduringposition-keeping.

Conclusion:

Inthispaper,anautonomousnavigationmethodforGEOorbitnavigationsatelliteposition-keepingwasproposed.Themethodutilizesthemeasurementsfrommultiplesensorstoestimatethesatellite'spositionandvelocity,andutilizessensorfusiontechniquestoimprovetheaccuracyofthepositionandvelocityestimates.ToreducethecomputationburdenoftheKalmanfilter,anadaptivefilteringapproachisproposed.Thesimulationre