摘要
Featurereductionisakeyprocessinpatternrecognition.Thispaperdealswiththefeaturereductionmethodsforatime-shiftinvariantfeature,powerspectrum,inRadarAutomaticTargetRecognition(RATR)usingHigh-ResolutionRangeProfiles(HRRPs).Severalexistingfeaturereductionmethodsinpatternrecognitionareanalyzed,andaweightedfeaturereductionmethodbasedonFisher'sDiscriminantRatio(FDR)isproposedinthispaper.AccordingtothecharacteristicsofradarHRRPtargetrecognition,thisproposedmethodsearchestheoptimalweightvectorforpowerspectraofHRRPsbymeansofaniterativealgorithm,andthusreducesfeaturedimensionality.Comparedwiththemethodofusingrawpowerspectraandsomeexistingfeaturereductionmethods,theweightedfeaturereductionmethodcannotonlyreducefeaturedimensionality,butalsoimproverecognitionperformancewithlowcomputationcomplexity.Intherecognitionexperimentsbasedonmeasureddata,theproposedmethodisrobusttodifferenttestdataandachievesgoodrecognitionresults.
出版日期
2006年03月13日（中国期刊网平台首次上网日期，不代表论文的发表时间）
