非同构环境中的分散估计

Z. Luo, Jinjun Xiao
{"title":"非同构环境中的分散估计","authors":"Z. Luo, Jinjun Xiao","doi":"10.1109/ISIT.2004.1365554","DOIUrl":null,"url":null,"abstract":"We consider the decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth constrained sensor network with a fusion center. We construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local Signal to Noise Ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that the local sensor compression schemes and final fusion function are independent of noise pdf. We show that its mean squared error is within a constant factor to that achieved by the classical centralized best linear unbiased estimator (BLUE).","PeriodicalId":269907,"journal":{"name":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Decentralized estimation in an inhomogeneous environment\",\"authors\":\"Z. Luo, Jinjun Xiao\",\"doi\":\"10.1109/ISIT.2004.1365554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth constrained sensor network with a fusion center. We construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local Signal to Noise Ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that the local sensor compression schemes and final fusion function are independent of noise pdf. We show that its mean squared error is within a constant factor to that achieved by the classical centralized best linear unbiased estimator (BLUE).\",\"PeriodicalId\":269907,\"journal\":{\"name\":\"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT.2004.1365554\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2004.1365554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

摘要

研究了带融合中心的带宽约束传感器网络对受噪声干扰的确定性参数的分散估计。我们构建了一种分散估计方案(DES),其中每个传感器将其观测值压缩为少量比特,其长度与局部信噪比(SNR)的对数成正比。然后由融合中心收集来自不同传感器的压缩比特并组合以估计未知参数。该算法具有普适性,局部传感器压缩方案和最终融合函数与噪声无关。我们表明,它的均方误差与经典集中式最佳线性无偏估计器(BLUE)的均方误差在一个常数因子内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decentralized estimation in an inhomogeneous environment
We consider the decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth constrained sensor network with a fusion center. We construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local Signal to Noise Ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that the local sensor compression schemes and final fusion function are independent of noise pdf. We show that its mean squared error is within a constant factor to that achieved by the classical centralized best linear unbiased estimator (BLUE).
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