Floor Identification Using Magnetic Field Data with Smartphone Sensors
Floor Identification Using Magnetic Field Data with Smartphone Sensors
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DOI：
10.3390/s19112538
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发表时间：
2019-06-01
期刊：
影响因子：
3.9
通讯作者：
Park, Yongwan
中科院分区：
文献类型：
--
作者：
Ashraf, Imran;Hur, Soojung;Park, Yongwan
楼层识别在多层室内定位系统中起着关键作用。目前的楼层识别系统主要依赖Wi-Fi信号和气压数据。气压系统需要安装额外的独立传感器来执行地板识别。另一方面，Wi-Fi系统容易受到动态环境以及路径损耗、阴影和多径衰落的不利影响。在本文中，我们利用普适磁场来补偿这些系统的局限性。我们使用智能手机传感器，使拟议的方案基础设施免费和成本效益高。我们使用智能手机磁传感器来识别多层建筑中的楼层，提高了精度。楼层识别通过正常行走、呼叫监听和电话摆动等用户活动来执行。利用各种机器学习技术来识别用户活动。通过大量的实验对所提出的基于磁数据的楼层识别方案进行了评估。此外，使用三星Galaxy S8、LG G6和LG G7智能手机研究了设备异构性对楼层识别的影响。研究结果表明，磁力地板识别技术的性能优于气压和Wi-Fi地板探测技术。加入换楼层模块，进一步提高楼层识别的准确性。
Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other hand, are vulnerable to the dynamic environment and adverse effects of path loss, shadowing, and multipath fading. In this paper, we take advantage of a pervasive magnetic field to compensate for the limitations of these systems. We employ smartphone sensors to make the proposed scheme infrastructure free and cost-effective. We use smartphone magnetic sensors to identify the floors in a multi-story building with improved accuracy. Floor identification is performed with user activities of normal walking, call listening, and phone swinging. Various machine learning techniques are leveraged to identify user activities. Extensive experiments are performed to evaluate the proposed magnetic-data-based floor identification scheme. Additionally, the impact of device heterogeneity on floor identification is investigated using Samsung Galaxy S8, LG G6, and LG G7 smartphones. Research results demonstrate that the magnetic floor identification outperforms barometric and Wi-Fi-enabled floor detection techniques. A floor change module is incorporated to further enhance the accuracy of floor identification.