Research On Unintentional Modulation Analysis And Recognition Methodology Of Radar Emitter

Radar Emitter Identification(REI)is to determine radar individual,usingextractingfeatures of received radar signals,by comparingthosefeatureswith a priorinformationlibrary．AsREI plays allimportant roleinthemilitary surveillance,ithasbecomethefoCUSofradarcountenneaanres research．Though the research on REI has been increasing recently,there are fewbreakthrough progresses．Most of the methods extract unintentional modulation onpulse(UMOP)feature just from signal phenomenon．Lacking of research on themechanism of UMOP or the form of unintentional modulation in signal,these makeextraction ofunintentional feature blind．Because UMOP is very subtle,its feature isvulnerabletoenvironmentalimpactsanditisdifficultto extractthe robustfeatures ofUMOP allthetime．We investigate the mechanism of generating PN in each component of MasterOscillator Power-Amplifier(M．0．P．A)transmitter frequency source from the analysisonUMOP．PN ofeach componentandthetotalPNoftheloopincludedactivefilterarerealized by studying the PN transferring characteristics of each component．Then,basedon the previous analysis,simulation tool ADS2009 is utilized to model the M．O．P．Atransmitter frequency source and investigate the PN charac~fistic and the effects ontotal PN of each component in detail,and then the total output PN distribution oftheloop ale obtained．Inorderto studythe effects ofsubtlePN,theoutputofthetotalPNisadded to ideal conventional radar signal．The simulation results indicate that there aredistinctdifferencesbetweenthe original signalandthe signal addedwithphasenoiseinthe phase,power spectrum and bispectrum although the added phase noise is very subtle．It can provide possibility to extract the feature ofUMOP．Finally,as the shape ofthepulse envelope can reflect the individual characteristics of radar emitter,three newfeatures,thefittingupangle,fittingdownangle aswell asthe ratio ofperimeterto area,are defined．Simulation results show that the new envelope features has betteraggregation than the traditional characteristics．Also the mean,the variance,theskewness,the kurtosisandtheenergyentropy ofthe proposed rotation components byintrinsictime-scaledecompositionareutilizedasthenovelfeaturefactorforthe suppoavector machine(SVM)classifier．The characteristic sets including envelope features ofthe new definition has better classification results atdifferent si~alto noise ratio．Allthe results are verified with the real measurement data generated by the uniform typeradar under the same way of work．It shows that a higher recognition rate could beobtained...