期刊论文详细信息
Sensors
Floor Identification Using Magnetic Field Data with Smartphone Sensors
Soojung Hur1  Imran Ashraf1  Muhammad Shafiq1  Yongwan Park1 
[1] Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea;
关键词: floor identification;    indoor localization;    machine learning;    smartphone sensors;    geomagnetism;    fingerprinting;   
DOI  :  10.3390/s19112538
来源: DOAJ
【 摘 要 】

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.

【 授权许可】

Unknown   

  文献评价指标  
  下载次数:0次 浏览次数:0次