Article(id=1242120018385764902, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242120017307828773, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1269360000000, receivedDateStr=2010-03-24, revisedDate=1271692800000, revisedDateStr=2010-04-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1274976000000, onlineDateStr=2010-05-28, pubDate=1274976000000, pubDateStr=2010-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1274976000000, onlineIssueDateStr=2010-05-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774074089855, creator=sys-migrate, updateTime=1774074089855, updator=sys-migrate, issue=Issue{id=1242120017307828773, tenantId=1146029695717560320, journalId=1146031591421210625, year='2010', volume='28', issue='10', pageStart='3', pageEnd='139', issueExtLink='null', onlineDate='null', pubDate='1274976000000', pubDateStr='2010-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774074089598, creator='sys-migrate', updateTime=1774074089598, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=101, endPage=105, ext={EN=ArticleExt(id=1242120021741208124, articleId=1242120018385764902, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Empirical Mode Decomposition and Its Application to Radar Signal, columnId=1242116810380743325, journalTitle=Science & Technology Review, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=In order to better estimate the instantaneous frequency of signals, the empirical mode decomposition (EMD) algorithm, proposed by Huang et al., is used to break multi-component signals into several narrow subbands. EMD is an adaptive method and can be used to analyze nonstationary signals, so it has been widely applied to many engineering fields. However, EMD is still considered as an empirical method because it lacks a rigorous mathematical foundation, and its analysis depends largely on numerical simulations and experimental investigations. In this paper, related problems of the EMD algorithm are discussed, including its theoretical foundation and its applications. Some modified EMD algorithms are considered to overcome problems, such as stopping criterion, end effect, envelope of signals and mode aliasing. The applications of EMD to the processing of radar signals are reviewed. Some directions for further research on the EMD algorithm are suggested., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=5vzlTFaRAi+lTZorOlshtw==, pdfFileSize=3167203, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242120020189319948, articleId=1242120018385764902, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=经验模态分解及其雷达信号处理, columnId=1242116809164390686, journalTitle=科技导报, columnName=综述文章, runingTitle=null, highlight=null, articleAbstract=为了准确估计信号的瞬时频率，可用经验模态分解（EMD）将信号分解成有限个窄带信号。该方法因具有很强的自适应性及处理非平稳信号的能力而引起广泛关注，已在众多工程领域得到应用。但EMD是基于经验的方法，数值仿真和试验研究仍是分析EMD算法的主要方法。本文总结了EMD算法存在的问题，并指出深入挖掘支持该方法的理论基础是消除制约EMD算法进一步发展和应用推广的关键。针对所存在的问题，从改进筛分停止准则、抑制端点效应、改进包络生成方法和解决模态混叠问题等诸方面阐述了改进EMD算法的研究进展。综述了EMD在雷达信号处理领域的应用。最后分析指出了进一步研究EMD的几个主要方向。, authors=^{杨彦利，邓甲昊}, authorsList=杨彦利;邓甲昊, authorCompany=北京理工大学机电学院；机电工程与控制重点实验室，北京 100081, correspAuthors=null, authorNote=null, correspAuthorsNote=杨彦利, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=2vHntil4Uk1D8gOMnPMlVw==, pdfFileSize=3167203, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, keywords=[Keyword(id=1242120019774083848, tenantId=1146029695717560320, journalId=1146031591421210625, 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