预览加载中，请您耐心等待几秒...

一种基于稀疏降维的STAP方法.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

一种基于稀疏降维的STAP方法 Title:SparseDimensionalityReduction-BasedSpace-TimeAdaptiveProcessing(STAP)Method Abstract: Space-TimeAdaptiveProcessing(STAP)isapowerfultechniqueusedfortargetdetectionandtrackinginairborneradarsystems.However,STAPoftenfaceschallengesofhighcomputationalcomplexityandthepresenceofinterferenceclutter.Inrecentyears,therehasbeenincreasinginterestinleveragingsparsedimensionalityreductiontechniquestoenhanceSTAPperformance.Thispaperpresentsareviewoftheexistingliteratureandproposesanovelsparsedimensionalityreduction-basedSTAPmethod.Theproposedmethodaimstoeffectivelyreducecomputationalburdenandmitigateinterferenceclutterwhilemaintainingtargetdetectionandtrackingaccuracy. 1.Introduction Space-TimeAdaptiveProcessing(STAP)isasignalprocessingtechniquewidelyusedinairborneradarsystemsfordetectingandtrackingtargetsinthepresenceofsevereclutterinterference.TraditionalSTAPmethodstypicallyrequirealargenumberoftrainingdatasamplesandhavehighcomputationalcomplexity.SparsedimensionalityreductiontechniquesofferapromisingsolutiontoaddressthesechallengesbyreducingthedimensionalityoftheSTAPdatawhileretainingtheimportantinformationnecessaryfortargetdetectionandtracking. 2.Background ThissectionprovidesacomprehensiveoverviewoftheconventionalSTAPtechniques,includingthebasicprinciples,challengesencountered,andpotentiallimitations.TheimportanceofdimensionalityreductioninSTAPisemphasized,leadingtothemotivationforexploringsparsedimensionalityreductionmethods. 3.SparseDimensionalityReductionTechniques Thissectionpresentsanin-depthdiscussionofvarioussparsedimensionalityreductiontechniquesthathavebeensuccessfullyappliedindifferentdomains.ThetechniquescoveredincludePrincipalComponentAnalysis(PCA),SparsePrincipalComponentAnalysis(SPCA),IndependentComponentAnalysis(ICA),andFactorAnalysis(FA).Thestrengths,weaknesses,andsuitabilityofeachtechniqueforSTAPapplicationsarediscussed. 4.SparseDimensionalityReduction-BasedSTAPMethod Inthissection,theproposedsparsedimensionalityreduction-basedSTAPmetho