在 UWB 定位中利用锚链路对抗 NLOS

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yijie Chen, Jiliang Wang, Jing Yang
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引用次数: 0

摘要

UWB(超宽带)已被证明是一种为物联网提供精确定位的前景广阔的技术。然而,在实际应用中,由于非视线(NLOS)问题,其性能大大降低。各种方法都或隐或显地探讨了这一问题。在本文中,我们提出了 RefLoc,利用 UWB 的独特优势来解决 NLOS 问题。我们发现,在同一环境中,NLOS 链路可能会有很大差异,而 LOS 链路则具有类似的特征,这些特征可以通过 UWB 的高带宽捕捉到。具体来说,RefLoc 的高级理念是首先识别已知位置锚点之间的链接,然后利用这些链接作为标签链接识别的参考。为了实现这一目标,我们解决了推导锚链接状态、提取合格链接特征以及利用锚链接推断标签链接等实际难题。我们在商用硬件上实现了 RefLoc,并在不同环境中进行了广泛的实验。评估结果表明,RefLoc 在各种环境下的平均 NLOS 识别准确率达到 96%,比最先进水平提高了 10%,并以很小的开销减少了 80% 的定位误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting Anchor Links for NLOS Combating in UWB Localization

UWB (Ultra-wideband) has been shown as a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this paper, we propose RefLoc that leverages the unique benefits of UWB to address the NLOS problem. While we find NLOS links can vary significantly in the same environment, LOS links possess similar features which can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
期刊介绍: ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.
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