基于高阶累积量的调制识别技术的研究

工学硕士学位论文基于高阶累积量的调制识别技术的研究金桂保哈尔滨工业大学2008年6月国内图书分类号:TN911.72国际图书分类号:621.396.972.1工学硕士学位论文基于高阶累积量的调制识别技术的研究硕士研究生：金桂保导师：田日才教授申请学位：工学硕士学科、专业：信息与通信工程所在单位：电子与信息技术研究院答辩日期：2008年7月授予学位单位：哈尔滨工业大学ClassifiedIndex:TN911.72U.D.C:621.396.972.1DissertationfortheMaster’sDegreeinEngineeringRESEARCHONTHETECHNOLOGYOFMODULATIONTYPESRECOGNITIONBASEDONHIGHERORDERCUMULANTCandidate：JinGuibaoSupervisor：Prof.TianRicaiAcademicDegreeAppliedfor：MasterofEngineeringSpecialty：InformationandCommunicationEngineeringAffiliation:SchoolofElectronicsandInformationTechnologyDateofDefence:July,2008Degree-Conferring-Institution:HarbinInstituteofTechnology哈尔滨工业大学工学硕士学位论文-I-摘要信号的调制识别是截获信号处理研究领域的一个十分重要的课题，它需要在复杂环境和有噪声干扰的条件下确定出信号的调制方式和调制参数，从而为进一步分析和处理信号提供依据。调制方式是区别不同性质信号的一个重要特征，调制识别的目的就是在没有其他先验知识的情况下，通过对接收到的信号进行处理，从而判断出信号的调制方式，并估计出相应的调制参数。随着通信技术的发展，空间中的信号越来越密集和复杂，通过接收机接收到的信号不再是单一的调制信号，这就加大了信号调制识别的难度，同时也对调制识别的研究提出了更高的要求。本文在前人工作的基础上，通过对信号累积量不变量特征的分析，深入研究了MPSK、MASK和MQAM数字信号的调制识别问题，主要工作可概括如下：(1)研究了MPSK、MASK和MQAM信号调制子类间的分类识别问题。通过对2/4/8PSK信号与MPSK、MASK和MQAM信号调制子类间的关系的考察表明，可以利用2/4/8PSK信号分类算法实现不同调制子类间的识别。对更高调制阶数的MPSK、MASK和MQAM信号分类进行了计算机仿真试验，仿真结果证实了子类分类算法的有效性。(2)研究了在高斯噪声和理想信道环境下，基于高阶累积量不变量特征的MPSK、MASK和MQAM信号调制分类识别算法，不变量特征对信噪比和未知的参考相位参数是盲的。讨论了分类算法的理论渐进性能，并通过大量的计算机仿真实验证实了分类算法的有效性。最后针对不同调制子类信号的特点，分别给出了递归降阶调制识别方法，从而在满足一定信噪比条件下，当观测数据长度足够长时，本文算法理论上可分类任意调制阶数的数字信号。关键词调制识别；特征提取；不变量分类特征；高阶累积量哈尔滨工业大学工学硕士学位论文-II-AbstractModulationidentificationforcommunicationsignalsisastillimportantproblemintheinterceptedsignalprocessing,itisrequiredtoidentifythemodulationformatandmodulationparametersinthecomplicatedsignalenvironmentwithnoise,andtoprovidereferenceforfartheranalysisandprocessing.Modulationformatisoneofthemostimportantcharacteristicsusedtodistinguishcommunicationsignals,Afteranalyzingthereceivedsignal,theobjectiveofmodulationidentificationistodecidethemodulationformatandestimatethemodulationparametersofthecommunicationsignalwithoutanyprioriknowledgeaboutthesignalinformationcontent.Withthedevelopmentofcommunicationtechnology,thespatialsignalsaremoreandmorecomplicatedanddense.Itresultsinthatoutputofthereceiverdoesn’tcontainsonlyonesignal,whichmakesthemodulationidentificationmoredifficult,Andasresults,therecomesmoredemandsfortheresearchofmodulationidentificationofcommunicationsignals.Basedontheanalysisofinvariantfeaturesincumulantdomainofcommunicationsignals,theclassificationofcommunicationsignalswithMPSK,MASKandMQAMmodulationformatsisinvestigatedthisdissertation.Themainworkscanbesummarizedasfollows:(1)Weinvestigatedsub-setclassificationproblembetweenMPSK,MASKandMQAMsignals.Examiningtherelationsbetween2PSK,4PSKand8PSKsignalsandMPSK,MASKandMQAMsignalsub-setrespectively,weconcludethatwecanrealizeMPSK,MASKandMQAMsignalssub-setclassificationalgorithmbyusingjustthesameonesthatrecognize2/4/8PSKsignals.ComputersimulationsofclassificationperformancebetweenMPSK,MASKandMQAMsignalswithmuchhighermodulationorderverifiedtheefficiencyofoursub-setclassificationalgorithms.Atthelastofthedissertation.(2)ThenewalgorithmsforclassificationofMPSK,MASKandMQAMsignalsusingcumulantinvariantsareproposedinGaussiannoiseandidealcommunicationchannelenvironment.TheclassificationfeaturesareblindtounknownSNRandreferencephase.Theasymptoticperformancesofour哈尔滨工业大学工学硕士学位论文-III-algorithmsareverifiedthroughtheoreticalanalysisandcomputersimulations.Accordingtotherespectivepropertiesofdifferentsignalsub-set,wegiverecursiveorder-reducingclassificationalgorithm.sothat,beyondcertainSNRandwithenoughreceiveddata,theoretically,ourclassificationalgorithmscanrecognizedigitalcommunicationsignalswithanymodulationorder.KeywordsModulationrecognition,featureextraction,Invariantclassificationfeature,Higherordercumulant哈尔滨工业大学工学硕士学位论文-IV-目录摘要...............................................................................................................................IAbstract.......................................................................................................................II第1章绪论................................................................................................................11.1概述...................................................................................................................11.2国内外相关领域的研究状况及分析...............................................................21.3本文主要研究的内容.......................................................................................6第2章高阶累积量理论基础....................................................................................72.1高阶矩和高阶累积量.......................................................................................72.2高阶累积量和高阶矩的转换关系.................................................................102.3高阶矩和高阶累积量的性质.........................................................................102.4平稳随机过程的高阶矩和高阶累积量.........................................................122.5数字信号的高阶累积量.................................................................................132.6本章小结........................................................................................