天线阵列优化研究

华中科技大学硕士学位论文天线阵列优化研究姓名：朱丹丹申请学位级别：硕士专业：电磁场与微波技术指导教师：李青侠20060428IOlen&Compton6dB1;Olen&Compton;;;;IIAbstractSyntheticaperturemicrowaveradiometerhasaverygoodforegroundinremotesensingandobjectdetecting.SyntheticapertureradiometerappliestheinterferometricprincipletosampleinthespatialfrequencydomainandretrievestheimagebyFourierorothernumericaltransformation.It’seasytodetectobject,butcomplextotrackobject.Adaptivearraycaneasilytrackobject,ifasystemcanusebothsyntheticapertureandadaptivearraytechnique,thesystemwillbebettertodetectandtrackobject.Thispaperstudieshowtoarrangeanadaptivearrayandhowtofindthebestweightsofanadaptivearrayunderasyntheticaperturearray.Firstthispaperstudiesthealgorithmofhowtoarrangeanadaptivearray:addingsomeantennainthesyntheticaperturearray,andusinggeneticalgorithmtodecidetheaddposition.Studyresultindicatesthatafterusinggeneticalgorithmtoarrangeanadaptivearray,thebiggestsidelobeofthearraycanfallthreedB.ThispaperalsostudiestheapplicationofOlen&Comptonalgorithminvariedlinearrays.StudyresultindicatesthatafterusingOlen&Comptonalgorithminuniformitylinearrayandlittlethinnedarray,thebiggestsidelobeofthearraycanfalltendB;afterusingOlen&Comptonalgorithminsyntheticaperturearray,thebiggestsidelobeofthearraycanfallonedB.Finally,thispapercombinedthegeneticalgorithmandtheOlen&Comptonalgorithmtogether,formingasynthesisalgorithm.Studyresultindicatesthatafterusingsynthesisalgorithminsyntheticaperturearray,thebiggestsidelobeofthearraycanfallsixdB,fallingoncethanjustusinggeneticalgorithmortheOlen&Comptonalgorithm.KeyWordsweightoptimization;Olen&Compton;thinnedarray;geneticalgorithm;simulatedannealing;syntheticaperture111.1[1],,[2][3],,[4-5][6-8]2,1.2[9-10][11]3SKOLNIK[12][13]PowellGeneticalgorithmGA[14-16]HAUPT[17-18]GA[19]Leechn1212n≺n1.2171.674IshiguroBlanton+n3030n≺[20-21][22-23][24-28]1946Dolph4[29-30]DolphTseng[31]Ngetal[32]Perini[33]OlenCompton1990IEEEAnumericalpatternsynthesisalgorithmforarrays[34]1999P.Y.Zhou[35]1.3Olen&Compton51.4Olen&Compton622.12-1Nxn()ngθn1n+ndθθ9090θ−≤≤xθ1d1Nd−2d2-1n()nxtx()()()12,,...,TNxtxtxt=x2-12-1Tnnw[]12,,...,TN=w2-2()()1Nnnnytwxt===∑Twx2-3n()()()()0()2njjtnnxtAegenNφθωψθ−+=≤≤2-4()()()112sin2nknkdnNφθπθλ−==≤≤∑2-57()nφθ()()()()()()()()023123,,,...,NTjtjjjNAeggegegeωψφθφθφθθθθθ+−−−=x2-6()()()()()()()23123,,,...,NTjjjNggegegeφθφθφθθθθθ−−−=v2-7v2-12-7()0jAeωψ+=xv2-8()()0jtytAeωψ+=Twv2-9()pθ=Twv2-102-102.2wwMeanSquareErrorSignal-to-NoiseRatioMaximumLikelihoodMinimumNoiseVariance8[36][34][37]SINR2-1xsfn=++xxxx2-11sxfxnxufn=+xxx2-12su=+xxx2-13w1us−RRmaxλqoptµ=wq2-14µuRsR[]TuuuE∗=Rxx2-15[]TsssE∗=Rxx2-162-141soptusµ−=wRv2-17svuRsRsθfθsAfAsψfψsvfv90()sjtsssAeωψ+=xv2-180()fjtfffAeωψ+=xv2-192[]TTssssssEA∗∗==Rxxvv2-202[]TTffffffEA∗∗==Rxxvv2-2112[(),(),...,()]TnNntntnt=x2-22()(1)intiN≤≤i2σ2[]TnnnEσ∗==RxxI2-23sxfxnx22TufnfffAσ∗=+=+RRRIvv2-242ssA=2ffA=2σ21σ=22sASNRsσ==2-2522fAINRfσ==2-26sRuRTssss∗=Rvv2-27Tufff∗=+RvvI2-28optwsoptwsf10()sθm12,,...,mfffmTssss∗=Rvv2-291mTTuffffif∗∗==+=+∑RvvIvFvI2-30[]12,,...,mdiagfff=F2-3112,,...,mffff=vvvv2-32n1,2,...,nsssnTTssssss∗∗==Rvvvsv2-33[]12,,...,mdiagsss=s2-3412,,...,mssss=vvvv2-35(1)isin≤≤(1)jfjm≤≤113LeechIshiguroBlantonSKOLNIKPowell3.1SASimulatedAnnealing[33],[38]3.1.1[39]43-1121d12d13d1d12d13d14d1d12d13d14d15d16d1d12d13d14d15d3-143-144n[]!2!(2)!nn−4n=6n44n3.1.2NnNnnN131)2)Nn012,,,......,NDDDD1)012,,,......,Npppp0pNp0DND1ND−1p1Np−0pNp2)3n−2N−231,,......,Nppp−3n−231,,......,Nppp−3-1()0/kkTex−3-10kkTx01k0kk0k3-1()0/01()()kkTfkekkT−=∞≺≺3-23-2k0()()EkkfkdkkT∞−∞==+∫3-3TT(0,)TTTkT0.1TT143.1.395019,,ppp5p234678,,,,,pppppp113212237,,ppp3p245678,,,,,pppppp2112111TT10()()EkkfkdkkT∞−∞==+∫k0k15p6p22p01269,,,,pppppTTT3-1430153-1n40146501479601261013701261014178014101618212390136132024282910013613202731353611013613202734384243120159162330374447495013013617202735454953575814012814203142535863666768150125101526374859657177787916012510152637485970768288899017012814203142536475869196991001011801281420314253647586971021071101111121901234550586571778389951011071121171212001249162324375063768910211512112713113213321012345659687479879410110811512212913313914522013915232437505863768910211512814114614815315716023013456111621354963779110511913314715616016917217324013678131520274359759110712313915516417317718418618825012341213223140577491108125142159176184192200205206207208260123581624324966831001171341511681851942032042132222232242252701241013192541577389105121137153169185201211214218221228231235236280123816232431486582991161331501671842012182272362452512542552562572901257929313251708910812714616518420322223823924324625525625926726927030012141622283642618099118137156175194213232251262264269275278280282285286287163.23.2.1GAGeneticAlgorithms()3.2.2[40-42]1[43-46]173.2.3n12[,,...,]Tnxxx=Xn(1,2...,)iXin=X12...nXXX=XiXXnn[47]1)010118[48]2)[49]3)1)1[50-51]2)219[52]3)30101103.2.4GA()GA5201)2)3)4)5)6)53-23-2213.2.50110GAGAHaupt1994Thinnedarraysusinggeneticalgorithms20100dB3-23-33-2dB11111111001-14.121111011101-11.13-3dB31101111001-9.741111011001-9.15101111