{"title":"基于快速相干信号子空间的宽带信号测向","authors":"M. Frikel, S. Bourennane","doi":"10.1109/DSPWS.1996.555529","DOIUrl":null,"url":null,"abstract":"A method to estimate the set of bias vectors spanning the signal subspace without eigendecomposition is described. Each basis vector can be determined by the Lanczos algorithm. The signal subspace estimates at each frequency are transformed by focusing matrices such that the coherent signal subspace will be constructed for all analysis bands. The performance of the proposed method is shown to be almost the same as that of the classical eigendecomposition method.","PeriodicalId":131323,"journal":{"name":"1996 IEEE Digital Signal Processing Workshop Proceedings","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Direction finding for wideband signals using fast coherent signal subspace\",\"authors\":\"M. Frikel, S. Bourennane\",\"doi\":\"10.1109/DSPWS.1996.555529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method to estimate the set of bias vectors spanning the signal subspace without eigendecomposition is described. Each basis vector can be determined by the Lanczos algorithm. The signal subspace estimates at each frequency are transformed by focusing matrices such that the coherent signal subspace will be constructed for all analysis bands. The performance of the proposed method is shown to be almost the same as that of the classical eigendecomposition method.\",\"PeriodicalId\":131323,\"journal\":{\"name\":\"1996 IEEE Digital Signal Processing Workshop Proceedings\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1996 IEEE Digital Signal Processing Workshop Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSPWS.1996.555529\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1996 IEEE Digital Signal Processing Workshop Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSPWS.1996.555529","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Direction finding for wideband signals using fast coherent signal subspace
A method to estimate the set of bias vectors spanning the signal subspace without eigendecomposition is described. Each basis vector can be determined by the Lanczos algorithm. The signal subspace estimates at each frequency are transformed by focusing matrices such that the coherent signal subspace will be constructed for all analysis bands. The performance of the proposed method is shown to be almost the same as that of the classical eigendecomposition method.