Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization

Cristian Rusu, J. Thompson, N. Robertson
{"title":"Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization","authors":"Cristian Rusu, J. Thompson, N. Robertson","doi":"10.1109/ICASSP.2017.7952769","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2017.7952769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.
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
通过l-1/l-∞范数最小化来平衡多个时间实例的传感器管理
在本文中,我们提出了一种多时间实例传感器管理问题的解决方案,以平衡传感器网络估计的准确性及其利用率。我们展示了这个问题如何简化为一个二元优化问题,我们给出了一个基于凸松弛的解决方案,该解决方案涉及正则化的重新加权的l_1范数的最小化。我们通过实验证明了所提出算法的行为,并将其与文献中先前的方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Enhancing observability in power distribution grids A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators Artificial bandwidth extension using the constant Q transform Salience based lexical features for emotion recognition Multicore distributed dictionary learning: A microarray gene expression biclustering case study
×
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