差分演进算法TDOA定位

摘要无线定位服务是一种有着广阔市场前景的移动增值业务，基本原理是利用现有蜂窝网络，通过对各种位置特征参数，包括到达时间(TOA)、到达时间差(TDOA)、到达方向(DOA)的测量和估计，来实现移动用户的定位。本论文对无线通信网络中基于TDOA的无线定位技术进行了研究。本文分析了国内外相关研究现状，给出了移动台定位的几种基本方法，并给出了TDOA定位的双曲线数学模型，分析了基于TDOA定位的Chan算法、遗传算法(GA)和差分演进算法(DE)，并对其进行了计算机仿真。仿真结果表明，三种算法各有优缺点：Chan算法定位精度较低但运算速度很快，GA算法和DE算法定位精度高但收敛时间较长。在上述研究的基础上，本论文提出了三种新的定位算法：基于TDOA的Chan-GA算法、Chan-DE算法和Chan-IDE算法。并在相同的仿真环境下进行比较，仿真结果表明，在保证种群数量的情况下，所提的算法性能稳定，能找到逼近全局最优点的解，相对于Chan算法精度更高,相对于以前的算法在保证收敛性能的前提下有更快的收敛速度。关键词：移动台定位；到达时间差；遗传算法；差分演进算法；免疫算法ABSTRACTCellularwirelesslocationserviceisanewmobilevalue-addedservicewithagoodmarketfuture.Itsbasicprincipleistoimplementmobileuserlocationthroughestimatingcharacteristicparametersrelativetoposition,includingtime-of-arrival(TOA),time-difference-of-arrival(TDOA),direction-of-arrival(DOA),etc.Thisthesisaimsattheresearchofwirelesslocationtechnologybasedontime-relatedmeasurementsinWirelessCommunicationSystem.Thethesisanalyzesthedomesticandforeigncorrelationresearchofpresentsituation,andgivesseveralessentialmethodsofmobilelocation.Afterthat,themathematicalmodelofTDOAhyperbolicequationsisestablished,threelocationalgorithmsbasedontime-difference-of-arrival(TDOA),Chan,geneticalgorithmandDifferentialEvolutionareanalyzed,andhavebeencarriedonthesimulationtothem.Thesimulationresultsshowthatallthealgorithmshavetheadvantagesanddisadvantages.TheChanalgorithmhasbadlocationaccuracyandveryquickoperatingspeed.Tothecontrary,thegeneticalgorithmandDifferentialEvolutionhaveahighaccuracyandafastconvergencetime.Basedontheaboveinvestigation,threenewlocationalgorithmscalledChan-GAalgorithm,Chan-DEalgorithmandChan-IDEalgorithmbasedonTDOAmeasurementsareputforward.Carryingonthecomputersimulationtothemunderthesameenvironment,thesimulationresultsshowthatifthepopulationsizeisbigenough,thealgorithmisrobustandcanfindthecoordinates.IthasahigheraccuracythanChanalgorithmsandafasterconvergencetimethangeneticalgorithm.Keywords:Mobilelocation;TDOA;Geneticalgorithm;DifferentialEvolution;Immunealgorithm目录第1章绪论············································································11.1课题研究背景···································································11.2课题研究的目的和意义······················································21.3国内外的研究现状····························································41.4本文的主要工作································································5第2章移动台定位的基本方法················································72.1移动台定位的两种方案······················································72.1.1基于网络的定位·······················································72.1.2基于移动台的定位····················································72.2移动台定位技术································································82.2.1基于场强测量的定位方法···········································82.2.2基于传播时间测量的定位方法·····································82.2.3基于信号到达角度测量的定位方法·····························102.2.4混合定位方法························································102.3影响移动台定位精度的主要原因·········································112.4本章小结·······································································12第3章基于TDOA定位算法的分析及仿真···························133.1TDOA定位的数学模型····················································133.1.1定位问题的最小二乘(LS)表示····································133.1.2TDOA双曲线模型··················································143.2TDOA定位算法——Chan算法··········································153.3定位准确率的评价指标····················································203.4本章小结·······································································21第4章遗传算法在TDOA定位中的应用·······························224.1遗传算法简介·································································224.1.1遗传算法的基本原理···············································224.1.2遗传算法的特点·····················································234.1.3遗传算法的基本流程图和主要步骤·····························244.1.4遗传算法的基本操作···············································254.2遗传算法在TDOA定位中的实现·······································274.2.1TDOA双曲线定位模型············································274.2.2改进的遗传算法的实现············································294.2.3Chan-GA算法的实现···············································324.3计算机仿真····································································324.4本章小结·······································································35第5章差分演进算法在TDOA定位中的应用··错误!未定义书签。5.1差分演进算法简介··························································365.1.1差分演进算法的基本原理·········································365.1.2差分演进算法的特点···············································375.1.3差分演进算法的优点···············································385.1.4差分演进算法的流程···············································385.1.5差分演进算法的参数选取·········································385.2差分演进算法在TDOA定位中的实现·································395.2.1差分演进算法的实现···············································365.2.2Chan-DE算法的实现···············································405.3计算机仿真····································································415.4本章小结·······························