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1、5118IEEETRANSACTIONSONSIGNALPROCESSING,VOL.58,NO.10,OCTOBER2010Time-FrequencyRepresentationBasedonanAdaptiveShort-TimeFourierTransformJingangZhongandYuHuangAbstract—Inthispaper,anewconcisealgorithmabouttime-fre-FortheconventionalTFRalgorithms,short-timeFourierquencyrepresentation(
2、TFR)basedonanadaptiveshort-timetransform(STFT),wavelettransform(WT),andWigner–VilleFouriertransform(ASTFT)ispresented.Inthisalgorithm,thedistribution(WD)arepopularchoices.However,thewindowanalysiswindowwidthisequaltothelocalstationarylengthwhichwidthofSTFTisinvariable,soitsuffersp
3、oortime-frequencyismeasuredbytheinstantaneousfrequencygradient(IFG)ofthesignal.Andtheinstantaneousfrequency(IF)ofthesignalisresolution.TheWTfailsbecauseitswindowdilationruleisnotobtainedbydetectingtheridgeofwavelettransform(WT).Thesignal-dependent(explainedafterwardsbythe?rstexamp
4、leASTFTprovidesmuchbetterperformancethanconventionalTFRofnumericalsimulation).TheStransform[2]stillinheritsalgorithms.Furthermore,thealgorithmissimplerandmorecom-thelimitationsofWT.TheWDmakes?neTFRinsomecaseputationalef?cientthansomeofotheradaptiveTFRalgorithmsofmonocomponentsigna
5、lanalysis.However,itintroducesproposedpreviously.Severalexamplesarepresentedtoillustrateitsbehaviorondifferentkindsofsignalsanddemonstrateitscross-termsbetweenmultiplesignalcomponents.Itmakesvalidity.interpretationoftheWDdif?cultinmanypracticalapplica-IndexTerms—Adaptivesignalproc
6、essing,Fouriertransform,tions.ThecrossWD(XWD)[3]waspresentedtoremedytime-frequencyanalysis.thede?ciencyofWD.However,thismethodrequiresiterantcomputation.Inrecenttwodecades,ithadbecomemoreandmoreap-I.INTRODUCTIONparentthatagoodTFRalgorithmshouldbeasignal-dependentone,andthenadaptiv
7、eTFRalgorithmswereproposed.SomeLASSICALFouriertransform(FT)isastandardtoolforoftheresearchersbelievethatnosinglekernelcangiveade-Cspectrumanalysisofstationarysignalbecauseitrevealsquateperformanceforalargeclassofsignals.Oneofthemde-theoverallfrequencycontent.However,foranonstation
8、ary?nedoptimalradialGaussiankerne
