{"title":"计算复厄米矩阵极值特征向量的新算法","authors":"J. Manton","doi":"10.1109/SSP.2001.955263","DOIUrl":null,"url":null,"abstract":"This paper presents a novel algorithm for computing the eigenvector associated with either the largest or the smallest eigenvalue of a complex Hermitian matrix. This type of algorithm is required for direction of arrival (DOA) and frequency estimation. Necessary and sufficient conditions for convergence are proved, and simulations show the superior performance over traditional methods.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"1 1","pages":"225-228"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A new algorithm for computing the extreme eigenvectors of a complex Hermitian matrix\",\"authors\":\"J. Manton\",\"doi\":\"10.1109/SSP.2001.955263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel algorithm for computing the eigenvector associated with either the largest or the smallest eigenvalue of a complex Hermitian matrix. This type of algorithm is required for direction of arrival (DOA) and frequency estimation. Necessary and sufficient conditions for convergence are proved, and simulations show the superior performance over traditional methods.\",\"PeriodicalId\":70952,\"journal\":{\"name\":\"信号处理\",\"volume\":\"1 1\",\"pages\":\"225-228\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"信号处理\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2001.955263\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"信号处理","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SSP.2001.955263","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new algorithm for computing the extreme eigenvectors of a complex Hermitian matrix
This paper presents a novel algorithm for computing the eigenvector associated with either the largest or the smallest eigenvalue of a complex Hermitian matrix. This type of algorithm is required for direction of arrival (DOA) and frequency estimation. Necessary and sufficient conditions for convergence are proved, and simulations show the superior performance over traditional methods.
期刊介绍:
Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.