多输入多输出系统辨识的正交最小噪声子空间

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955278

A. Safavi, K. Abed-Meraim

{"title":"多输入多输出系统辨识的正交最小噪声子空间","authors":"A. Safavi, K. Abed-Meraim","doi":"10.1109/SSP.2001.955278","DOIUrl":null,"url":null,"abstract":"This contribution deals with a particular family of blind system identification techniques, referred to as minimum noise subspace (MNS) method. The MNS method is a computationally fast version of the subspace method. We develop an orthogonal version of MNS method. The orthogonal minimum subspace (OMNS) method is more efficient in computation than a standard subspace method, and is more robust to channel noise than MNS.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"78 1","pages":"285-288"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Orthogonal minimum noise subspace for multiple-input multiple-output system identification\",\"authors\":\"A. Safavi, K. Abed-Meraim\",\"doi\":\"10.1109/SSP.2001.955278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This contribution deals with a particular family of blind system identification techniques, referred to as minimum noise subspace (MNS) method. The MNS method is a computationally fast version of the subspace method. We develop an orthogonal version of MNS method. The orthogonal minimum subspace (OMNS) method is more efficient in computation than a standard subspace method, and is more robust to channel noise than MNS.\",\"PeriodicalId\":70952,\"journal\":{\"name\":\"信号处理\",\"volume\":\"78 1\",\"pages\":\"285-288\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"信号处理\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2001.955278\",\"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.2001.955278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

这篇文章讨论了一类特殊的盲系统识别技术，即最小噪声子空间(MNS)方法。MNS方法是一种计算速度快的子空间方法。我们开发了一种正交版本的MNS方法。正交最小子空间(OMNS)方法的计算效率比标准子空间方法高，对信道噪声的鲁棒性比MNS方法强。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Orthogonal minimum noise subspace for multiple-input multiple-output system identification

This contribution deals with a particular family of blind system identification techniques, referred to as minimum noise subspace (MNS) method. The MNS method is a computationally fast version of the subspace method. We develop an orthogonal version of MNS method. The orthogonal minimum subspace (OMNS) method is more efficient in computation than a standard subspace method, and is more robust to channel noise than MNS.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

信号处理

自引率

0.00%

发文量

5812

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.