Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

被引:21
|
作者
Liu, Liu [1 ]
Li, Bofeng [1 ]
Yang, Ling [1 ]
Liu, Tianxia [1 ]
机构
[1] Tongji Univ, Coll Surveying & GeoInformat, Shanghai 200092, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 06期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
indoor positioning; iBeacon-based positioning; PDR (pedestrian dead reckoning); data fusion; smartphone sensors; LOCALIZATION;
D O I
10.3390/app10062003
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.
引用
收藏
页数:20
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