Department of Information and Communication Engineering, Yeungnam Univeristy, Gyeongsan, Gyeongbuk 38541, Korea.
Sensors (Basel). 2019 Mar 27;19(7):1496. doi: 10.3390/s19071496.
Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.
基于指纹的 Wi-Fi 定位被认为是室内定位领域中应用最广泛的技术。指纹数据库是 Wi-Fi 定位系统的重要组成部分。然而，校准的离线阶段涉及到现场分析的一项繁琐任务，这涉及到成本和时间的浪费。我们提供了一种基于室内环境的无线电地图自动生成的室内定位系统。所提出的系统不需要任何努力，并且使用 Wi-Fi 兼容的物联网 (IoT) 传感器。传播损耗参数是从部署传感器的在线反馈中自动估计的，并且无线电地图会定期更新，而无需任何物理干预。所提出的系统利用环境的光栅地图和墙壁信息，而不是基于矢量地图的计算密集型技术，后者需要每个墙壁的长度和角度的精确信息。实验结果表明，所提出的系统实现了平均 2 米的精度，这与基于调查的 Wi-Fi 指纹技术相当。
