分布式检测的可扩展多水平量化

G. Gul, Michael Basler
{"title":"分布式检测的可扩展多水平量化","authors":"G. Gul, Michael Basler","doi":"10.1109/ICASSP39728.2021.9414032","DOIUrl":null,"url":null,"abstract":"A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions. The problem formulation makes it possible to consider globally optimum error minimization at the fusion center and a person-by-person optimum quantization at each sensor. The complexity of the algorithm is quasi-linear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current state-of-the-art.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"18 1","pages":"5200-5204"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Scalable Multilevel Quantization for Distributed Detection\",\"authors\":\"G. Gul, Michael Basler\",\"doi\":\"10.1109/ICASSP39728.2021.9414032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions. The problem formulation makes it possible to consider globally optimum error minimization at the fusion center and a person-by-person optimum quantization at each sensor. The complexity of the algorithm is quasi-linear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current state-of-the-art.\",\"PeriodicalId\":6443,\"journal\":{\"name\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"volume\":\"18 1\",\"pages\":\"5200-5204\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP39728.2021.9414032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP39728.2021.9414032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在分布式传感器网络中,多个传感器向融合中心发送其观测值的汇总信息以进行最终决策,提出了一种可扩展的传感器观测值多级量化算法。该算法直接最小化网络的整体错误概率,而不需要最小化伪目标函数(如概率分布之间的距离)。该问题的表述可以考虑融合中心的全局最优误差最小化和每个传感器的逐人最优量化。该算法的复杂度是准线性的。实验结果表明,该方案优于目前的技术水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Multilevel Quantization for Distributed Detection
A scalable algorithm is derived for multilevel quantization of sensor observations in distributed sensor networks, which consist of a number of sensors transmitting a summary information of their observations to the fusion center for a final decision. The proposed algorithm is directly minimizing the overall error probability of the network without resorting to minimizing pseudo objective functions such as distances between probability distributions. The problem formulation makes it possible to consider globally optimum error minimization at the fusion center and a person-by-person optimum quantization at each sensor. The complexity of the algorithm is quasi-linear for i.i.d. sensors. Experimental results indicate that the proposed scheme is superior in comparison to the current state-of-the-art.
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