基于阵列平均分数阶傅里叶变换的盲源分离

Lu-ping Zhou, Bingrong Li, Chun-feng Wang
{"title":"基于阵列平均分数阶傅里叶变换的盲源分离","authors":"Lu-ping Zhou, Bingrong Li, Chun-feng Wang","doi":"10.1109/CCDC.2009.5194850","DOIUrl":null,"url":null,"abstract":"In this paper, a novel blind source separation method based on the array-averaged Fractional Fourier Transform is proposed. This method can decrease the noise levels, and suppress the interactions of the source signals, which lead to better separation performance. Compared with the previous blind source separation techniques based on the time-frequency distributions, this proposed method produces little crossterms, and it does not require whitening, joint-diagonalization, and bilinear signal synthesis. The improved efficiency of the method is verified by the simulation.","PeriodicalId":127110,"journal":{"name":"2009 Chinese Control and Decision Conference","volume":"259 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Blind source separation based on the array-averaged Fractional Fourier Transform\",\"authors\":\"Lu-ping Zhou, Bingrong Li, Chun-feng Wang\",\"doi\":\"10.1109/CCDC.2009.5194850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a novel blind source separation method based on the array-averaged Fractional Fourier Transform is proposed. This method can decrease the noise levels, and suppress the interactions of the source signals, which lead to better separation performance. Compared with the previous blind source separation techniques based on the time-frequency distributions, this proposed method produces little crossterms, and it does not require whitening, joint-diagonalization, and bilinear signal synthesis. The improved efficiency of the method is verified by the simulation.\",\"PeriodicalId\":127110,\"journal\":{\"name\":\"2009 Chinese Control and Decision Conference\",\"volume\":\"259 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Chinese Control and Decision Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCDC.2009.5194850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Chinese Control and Decision Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2009.5194850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种基于阵列平均分数阶傅里叶变换的盲源分离方法。该方法可以降低噪声水平,抑制源信号的相互作用,从而获得更好的分离性能。与以往基于时频分布的盲源分离技术相比,该方法产生的交叉项较小,且不需要白化、联合对角化和双线性信号合成。通过仿真验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind source separation based on the array-averaged Fractional Fourier Transform
In this paper, a novel blind source separation method based on the array-averaged Fractional Fourier Transform is proposed. This method can decrease the noise levels, and suppress the interactions of the source signals, which lead to better separation performance. Compared with the previous blind source separation techniques based on the time-frequency distributions, this proposed method produces little crossterms, and it does not require whitening, joint-diagonalization, and bilinear signal synthesis. The improved efficiency of the method is verified by the simulation.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信