An accurate clock offset estimation method between two moving objects based on maximum likelihood estimation

Kousuke Matsui, S. Hara
{"title":"An accurate clock offset estimation method between two moving objects based on maximum likelihood estimation","authors":"Kousuke Matsui, S. Hara","doi":"10.23919/APCC.2017.8303959","DOIUrl":null,"url":null,"abstract":"We consider a problem of accurate clock offset estimation between two moving objects. To establish clock synchronization between objects, the principle of the Two Way Ranging (TWR) method has been commonly used in the Network Time Protocol (NTP), the IEEE 1588 standard and so on. In this paper, we first reveal that they do not perform accurately at all when two objects are in motion. Next, to solve the problem, we propose an accurate clock offset estimation method. In the proposed method, we utilize the location information on the two objects as well, and taking into consideration that the measured distance between them is not Gaussian-but Rician-distributed, we strictly derive the Maximum Likelihood estimation (ML) on the clock offset. Then, to reduce the computational complexity of the strict ML method, we introduce two approximated ML methods. In computer simulation, we show that the proposed method outperforms conventional methods, achieving the clock offset estimation accuracy of less than several tens of nanoseconds, which is required for current and future wireless communication tools among moving objects.","PeriodicalId":320208,"journal":{"name":"2017 23rd Asia-Pacific Conference on Communications (APCC)","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 23rd Asia-Pacific Conference on Communications (APCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/APCC.2017.8303959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

We consider a problem of accurate clock offset estimation between two moving objects. To establish clock synchronization between objects, the principle of the Two Way Ranging (TWR) method has been commonly used in the Network Time Protocol (NTP), the IEEE 1588 standard and so on. In this paper, we first reveal that they do not perform accurately at all when two objects are in motion. Next, to solve the problem, we propose an accurate clock offset estimation method. In the proposed method, we utilize the location information on the two objects as well, and taking into consideration that the measured distance between them is not Gaussian-but Rician-distributed, we strictly derive the Maximum Likelihood estimation (ML) on the clock offset. Then, to reduce the computational complexity of the strict ML method, we introduce two approximated ML methods. In computer simulation, we show that the proposed method outperforms conventional methods, achieving the clock offset estimation accuracy of less than several tens of nanoseconds, which is required for current and future wireless communication tools among moving objects.
一种基于极大似然估计的运动对象间时钟偏移的精确估计方法
我们考虑了两个运动物体之间精确的时钟偏移估计问题。为了在对象之间建立时钟同步,双向测距(TWR)方法的原理已被广泛应用于网络时间协议(NTP)、IEEE 1588标准等。在本文中,我们首先揭示了当两个物体处于运动状态时,它们根本不能准确地执行。接下来,为了解决这个问题,我们提出了一种精确的时钟偏移估计方法。在该方法中,我们同时利用了两个目标的位置信息,并考虑到它们之间的测量距离不是高斯分布而是里氏分布,我们严格推导了时钟偏移的最大似然估计(ML)。然后,为了降低严格机器学习方法的计算复杂度,我们引入了两种近似机器学习方法。在计算机仿真中,我们证明了该方法优于传统方法,实现了小于几十纳秒的时钟偏移估计精度,这是当前和未来移动物体之间无线通信工具所需要的。
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 求助全文
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