基于大型展会参与者间分布的BLE标签的位置估计方法

Kenta Urano, Kei Hiroi, K. Kaji, Nobuo Kawaguchi
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引用次数: 6

摘要

室内位置估计是分析大型展会参与者活动的关键技术。现有的PDR、超声、激光测距等方法存在测量设备在大场地的安装位置、测量设备的成本、智能手机应用的必要性等问题。我们专注于低功耗蓝牙(BLE)。目前,BLE技术被用于智能手机的距离通知,无法检测到两个BLE设备之间的确切距离。这是因为BLE无线电波不稳定,在相同的距离上,每次信号强度都会发生变化。在本文中,我们提出了一种利用BLE信标标签和单板计算机的位置估计方法。与使用位置固定的BLE信标的传统方式不同,BLE信标标签在活动中分发给参与者。利用多个定位扫描器捕获的BLE广告数据包的信号强度进行定位估计。该方法需要较少的设备成本和安装应用程序的人工成本。无需复杂的初始设置即可使用。我们在真实的大型展览中进行了数据收集实验。我们通过将所提出的方法应用于收集的数据来估计参与者的位置,然后评估估计的准确性,并根据参与者的职业分析活动,如停留时间较长的摊位。因此,我们可以跟踪参与者的运动到一些米误差,并根据每个职业的长期停留摊位找到特征摊位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Location Estimation Method using BLE Tags Distributed Among Participants of a Large-Scale Exhibition
Indoor location estimation is essential technology when we analyse the participants' activities in large-scale exhibition. There are some problems with existing methods such as PDR, ultrasound and laser range finder: installation location of measurement equipment at large site, cost for measurement equipment, and necessity of smartphone application. We focus on Bluetooth Low Energy(BLE). Currently, BLE technology is used as proximity notification for smartphones and cannot detect the exact distance between two BLE devices. This is because BLE radiowave is unstable and signal strength changes every time at the same distance. In this paper, we propose a location estimation method which utilizes BLE beacon tags and single board computers. In contrast to conventional ways using location-fixed BLE beacons, BLE beacon tags are distributed to participants at the event. Signal strengths of BLE advertising packets captured by multiple location-fixed scanners are used for location estimation. The method requires less cost for equipment and labor of installing an application. It can be used without complex initial setting. We had a data collection experiment at real large-scale exhibition. We estimate locations of participants by applying the proposed method to the collected data, then evaluate accuracy of estimation and analyse the activities such as the time of longer stay booths based on participants' occupation. As a result, we could track the movement of the participants to some meters error and find the characteristic booths based on longer-stay booths per occupations.
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