体内医疗环境下的球形囊状微型机器人的无线定位

上海交通大学硕士学位论文体内医疗环境下的球形囊状微型机器人的无线定位姓名：任峙宗申请学位级别：硕士专业：计算机体系结构指导教师：祝永新20081201ITDOAPCBProtelTDOATDOAPCBIIWIRELESSPOSITIONINGOFMICROCAPSULE-SHAPEROBOTINBODYAbstractOurprojectcamefromthemedicine-industryprojectaboutmicrorobotwhichcanexaminhumanbody.Ourresearchismainlyaboutwirelesspositioninginbody.Focusingonthespecialenvironmentinhumanbody,atfirst,thispaperdiscussedtheimportanceabouttheexaminationinbodywithmicrocapsulerobot,andanalyzedthestatementofthisresearchinandoutnation.Becausethisresearchisatthebeginningstage,wecanbelievethatithashugedevelopmentspacewithinalongtime.Afteranalyzingandcomparingavarietyofpositioningmethods,thispaperraiseakindofwirelesspositioningbasedonphasedifferenceofseveralpoints,anddescribedthedesigningplanandprinciple.SincetheTDOAarithmeticisthemainpositioningarithmeticofthissystem,wehavedeepresearchatthisarithmetic,includinggettingthedifferenceandsolvingthepositioningequation.Thenwehavesomeresearchonantenna.Wechosethehelicalantennaaccordingtothesystemrequirementtodesignandsimulate.DSPcircuitisveryimportantandalsodifficult.OurprojecthasdetailedresearchoneverymoduleoftheDSPcircuit,andsimulationofthemodule.TheycanbereferencetodesignPCBboardlater.ThereshouldbeanimpedancematchcircuitbetweenantennaandDSPcircuit,sowedesignedtheimpedancematchcircuittoimprovethereceivingefficiency.Finallyafterthedesignandsimulation,weusedPROTELtodrawthecircuitsofthewholesystem,thenbroughtthemtofactorytoproduce,andhadsometestonthem.Allinall,thisprojecthasstudiedthewirelesspositioningsystem,whichisbasedonthethree-dimensionalspace,tosettlethekeyissuesonthemicro-robotcapsuleinvivoenvironment.Theprojectwillpromotetheprocessofmicro-robotcapsule'sapplication,andhelptoimplementthecapsulewirelesspositiontechnologyinhumanbody.Keywords:WirelessPosition,TDOA,Antenna,DSP,ImpedanceMatch,PCB20092220092220092211.1SOCRFCT[15]1.1.1[26]11Fig.1MicroRobotofZheJiangUniversity2GivenImagingPillCam2PillCamSB60PillCamESOPillCamAgile(TM)PillCamCOLON(UnitedStatesFoodandDrugAdministration)600,000PillCamGivenImagingYoqneam2GivenImagingPillCamFig.2PillCanofGivenImaging33Fig.32-DPositioning31.1.2[28]12344561.22060(GPS)(GPS)2070202001994GPS24GPS12CDMA::;GPSGPSWi-FiUWBRFIDWi-Fi:Airespace2004Wi-Fi[[1,802.11Wi-FiGPSWi-F,2040Wi-Fi5Wi-Fi90MUWB(UltraWide-Band,):UWB4UWBIEEE802.15.4aUWBRFID():()()RFIDRFID3G/GSM/WCDMA:LCS(LocationServices)()GSM100m[20]1Tab.1ComparisonofPositioningTechnologiesGPSWi-FiUWB(UltraWide-Band,)RFID.10m1.36(SOS,StrengthofSignal)(AOA,AngleofArrival)(TOA,TimeofArrival)(TDOA,TimeDifferenceofArrival)1.3.1(SOS)SOSOkumura,HatsSOS1.3.2(AOA)4~12AOAAOAAOAAOA1.3.3(TOA)TOATOATOATOAGPS7CDMATOATOAlus3001.3.4(TDOA)TDOATDOA()TDOATDOA1.41.4.1[29]81.4.21RFID2.22.4GHz2.45GHz[26]23GPSCT12345PCB1.592.14Fig.4PositionSystem(Target,U)(Sj)TDOA3D5Fig.5SchematicFigureofWirelessPositioningSystem1012f100MHz3TDOA42.2cUS1S2t1t2(t1t2)US1S2∆D=c*(t1-t2)(2.1)6Fig.6SignalTransmitFromLaunchertoReceiverUS1S2US1S2S11ϕS22ϕ12ϕϕϕ∆=−(2.2)11t1-t2=2fϕπ∆(2.3)22Dcfϕλϕππ∆∆∆=⋅=(2.4)TDOA1TDOA2.312TDOA3.1TDOATDOA7TDOATDOAFig.7TDOAPositioningSystemTDOATDOA.TDOA()TDOA13(NLOSNon-LineofSight)TDOA3.28TDOAFig.8TDOAPositioningMethod3.2TDOATDOATOATDOATOATOATDOATDOA:14TDOA3.2.1(AsymptoticMaximumLikelihoodEstimation,AML),(ExactMaximumLikelihood,EML),EM(Estimate-Maximum)(WaveletDenoising,WD)(GeneralizedCross-CorrelationMethod)TDOA1):11()()()xtstnt=+(3.1)22()()()xtstDntα=++(3.2)s(t)1n1n(AWGN,AdditiveWhiteGaussianNoise),DTDOA2)99Fig.9MethodModel3)15:1212()[()()]xxRExtxtττ=−(3.3):12121()()()TxxRxtxtdtTττττ=−−∫(3.4)T1x2x1()Hf2()Hf:12122()()jfxxxxRRfedfππτ∞−∞=∫(3.5)1()xt2()xt:1212*12()()()yyxxGfHfHGf=(3.6)1()xt2()xt:1212()2()()()gjfyygxxRfGfedfπττψ∞−∞=∫(3.7)*12()()()gfHfHfψ=(3.8)4):121212()()()xxssnnRRDRταττ=−+(3.9):1212122()()()jfxxssnnGfGfeGfπτα−=+(3.10)1()nt2()nt12()nnGf=01()xt2()xt:1212()()()xxssRRtDτατδ=⊗−(3.11)16⊗()(0)ssssRRτ≤D:1212()()()xxssiiiRRtDτταδ=⊗−∑(3.12)TDOA()gfψ()gfψ5)1()Hf2()Hf2Tab.2ParametersofFourFilters17TDOA7:maxcTdτ≥+:TdmaxTDOATDOA1()Hf2()Hf(EML)1):x(t;d)=a(t;d)+n(t),0tT(3.13),(3.13):12()()(;),()()()ntatatdntatdnt==−,(3.14)x(t,d)a(t;d)n(t)2)211(;)()()1Tiiidlxdtytdtλψλ∞=−=+∑∫(3.15)2121(),0()()(),0(),xtddtytxtxtdtTdxtTdtt+−≤=++≤−−≤(3.16)iλ()itψa(t;d):(;)()()(),0TaiiidRtuduluduttTψλψ−−=≤≤∫(3.17)181,0()2,01,dtlttTdTdtt−≤=≤−−≤(3.18)3)EML10EMLFig.10EstimateModelofEML10EML:maxcTdτ≥+TDOAWDWDTDOA3.511WDFig.11WDEstimateSyetemTDOAWD19EM(AML)(EML)EMEME-StepM-StepE-StepM-StepE-StepE-StepM-Step12EMFig.12EMStructureFigureEM12()irtiEM3.2.2TDOAMCTDE(MultipathCancellationTimeDelayEstimation)METDE(MultipathEqualizationTimeDelayEstimation).1)20METDEMETDE11()()()xkskskα=+−∆(3.19)22()()()ykskDskDα=−+−−∆(3.20)s(k)D1α2α1∆2∆2):11()()sin()()ixkskciskiα∞=−∞=+−∆−∑(3.21)22()()sin()()jykskDciskDjα∞=−∞=−+−∆−−∑(3.22)Pz:11()(1())()XzzSzα=+∆(3.23)22()(1())()()YzzSzDzα=+∆(3.24):()sin()pnnpDzcnDz−=−=−∑(3.25)1()sin()piipzciDz−=−∆=−∑(3.26)2()sin()pjjpzcjDz−=−∆=−∑(3.27)METDEMETDE1314:2113MCTDEFig.13MCTDEArithmeticModel14METREFig.14METREArithmeticModelMCTDEMETREMCT