基于高阶统计量的快速宽带源分离

S. Bourennane, M. Frikel, A. Bendjama
{"title":"基于高阶统计量的快速宽带源分离","authors":"S. Bourennane, M. Frikel, A. Bendjama","doi":"10.1109/HOST.1997.613546","DOIUrl":null,"url":null,"abstract":"In this paper we develop an algorithm to improve the accuracy of the estimation of the direction of arrival of the wide-band sources. It is well known that when the noise cross-spectral matrix is unknown, these estimates may be grossly inaccurate. Using both the fourth order cumulant for suppression of the Gaussian noise, the transformation matrices for estimating the coherent signal subspace and a noneigenvector algorithm a robust method for the source characterisation problem in the presence of noise with an unknown cross-spectral matrix is developed. We show that the performance of bearing estimation algorithms improves substantially when our robust algorithm is used. Simulation results are presented for the unknown noise spectral matrix.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Fast wideband source separation based on higher-order statistics\",\"authors\":\"S. Bourennane, M. Frikel, A. Bendjama\",\"doi\":\"10.1109/HOST.1997.613546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we develop an algorithm to improve the accuracy of the estimation of the direction of arrival of the wide-band sources. It is well known that when the noise cross-spectral matrix is unknown, these estimates may be grossly inaccurate. Using both the fourth order cumulant for suppression of the Gaussian noise, the transformation matrices for estimating the coherent signal subspace and a noneigenvector algorithm a robust method for the source characterisation problem in the presence of noise with an unknown cross-spectral matrix is developed. We show that the performance of bearing estimation algorithms improves substantially when our robust algorithm is used. Simulation results are presented for the unknown noise spectral matrix.\",\"PeriodicalId\":305928,\"journal\":{\"name\":\"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HOST.1997.613546\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOST.1997.613546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

本文提出了一种提高宽带源到达方向估计精度的算法。众所周知,当噪声交叉谱矩阵未知时,这些估计可能非常不准确。利用四阶累积量抑制高斯噪声、变换矩阵估计相干信号子空间和非特征向量算法,提出了具有未知交叉谱矩阵的噪声存在下的源表征问题的鲁棒方法。我们表明,当使用我们的鲁棒算法时,轴承估计算法的性能大大提高。给出了未知噪声谱矩阵的仿真结果。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Fast wideband source separation based on higher-order statistics
In this paper we develop an algorithm to improve the accuracy of the estimation of the direction of arrival of the wide-band sources. It is well known that when the noise cross-spectral matrix is unknown, these estimates may be grossly inaccurate. Using both the fourth order cumulant for suppression of the Gaussian noise, the transformation matrices for estimating the coherent signal subspace and a noneigenvector algorithm a robust method for the source characterisation problem in the presence of noise with an unknown cross-spectral matrix is developed. We show that the performance of bearing estimation algorithms improves substantially when our robust algorithm is used. Simulation results are presented for the unknown noise spectral matrix.
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
期刊最新文献
Narrow band source separation in wide band context applications to array signal processing Higher-order statistics for tissue characterization from ultrasound images An iterative mixed norm image restoration algorithm Comparison between asymmetric generalized Gaussian (AGG) and symmetric-/spl alpha/-stable (S/spl alpha/S) noise models for signal estimation in non Gaussian environments Linear algebraic approaches for (almost) periodic moving average system identification
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1