用于实现零开销wifi室内定位系统的激光测距扫描仪

IF 1.2 Q4 REMOTE SENSING
Hamada Rizk, H. Yamaguchi, Maged A. Youssef, T. Higashino
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引用次数: 10

摘要

在过去的十年中,稳健和准确的室内定位一直是一些研究工作的目标。为了实现这一目标,已经提出了基于WiFi指纹的室内定位系统。但是,指纹识别需要大量的工作—特别是在高密度的情况下—并且需要在部署区域发生任何变化时重复进行。虽然最近已经引入了许多系统来减少校准工作,但这些系统仍然以准确性为代价。本文介绍了LiPhi++,这是一个精确的系统,可以实现基于指纹的室内定位系统,而不需要相关的数据收集开销。这是通过利用便携式激光测距扫描仪的传感能力来自动标记WiFi扫描,随后可用于建立(和维护)指纹数据库来实现的。作为其设计的一部分,LiPhi++利用这个数据库来训练一个深度的长短期记忆网络,利用来自检测到的接入点的信号强度历史。LiPhi++还提供了处理实际部署问题的规定,包括嘈杂的无线环境、异构设备等。使用Android手机在两个实际测试平台上对LiPhi++进行的评估表明,在相同的部署条件下,它可以匹配手动指纹识别技术的性能,而不会产生与传统指纹识别过程相关的开销。此外,在使用几个月后收集的数据进行测试时,LiPhi++在基于众包系统和基于指纹系统的定位精度中值基础上分别提高了284%和418%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System
Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++, an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.
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来源期刊
CiteScore
4.40
自引率
5.30%
发文量
43
期刊介绍: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.
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