时基室内定位系统的 NLOS 识别与缓解:调查与未来研究方向

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Raphael Elikplim Nkrow, Bruno Silva, Dutliff Boshoff, Gerhard Hancke, Mikael Gidlund, Adnan Abu-Mahfouz
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引用次数: 0

摘要

使用基于时间的网络进行精确室内定位的一个障碍是存在非视线(NLOS)和多径信号,这影响了室内环境中的测距精度。多年来,人们对 NLOS 识别和缓解进行了研究,并将其应用于不同的时基网络,其中大多数工作都考虑了与 WiFi 和 UWB 信道的 NLOS 链接。在本文中,我们将讨论 NLOS 条件对室内定位的影响和挑战,并介绍目前文献中最先进的 NLOS 识别和缓解方法。我们对这些方法进行了调查,并将它们分为不同类别,同时介绍了它们的优缺点。在这项工作中,我们进一步将解决 NLOS 影响的方法分为基于测量的单一方法和混合方法。本文还介绍了从调查中吸取的经验教训以及未来的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions

One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the years and applied to different time-based networks, with most works considering NLOS links with WiFi and UWB channels. In this paper, we discuss the effects and challenges of NLOS conditions on indoor localization and present current state-of-the-art approaches to NLOS identification and mitigation in literature. We survey these approaches and classify them under different categories together with their merits and demerits. We further categorize approaches to tackle NLOS effects into single and hybrid measurement-based approaches in this work. Lessons learnt from the survey with future directions are also presented in this paper.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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