# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 6 Conclusions  
This paper proposes a low-cost and real-time approach of indoor positioning based on iBeacon and PDR. It consists of several parts: (1) the BLE-based method of iBeacon, which fuses trilateration and fingerprinting; (2) a PDR method that considers filtering on heading orientation; and (3) the fusion approach of iBeacon and PDR to correct 'jumping' positions and PDR drifting error.  
This approach was implemented in an Android smartphone, and we conducted real-time positioning experiments in a building. Real-time tracking tests were conducted on routes with sharp turns, and we considered the errors in initial positions and heading orientations. The experiments show the positioning accuracy of our BLE-based method is the RMSE of 2.75 m. The tests with inaccurate initial positions in two routes result in the RMSE of 2.39 and 2.22 m, respectively; while the other tests with disturbed orientations result in the RMSE of 3.48 and 3.66 m for the two routes. The results demonstrate our fusion method can improve the accuracy of real-time trajectories and alleviate the influence of inaccurate initial positions, systematic drifting, and orientation noises. The BLE-based positioning method provides the baseline accuracy, and the PDR method smooths real-time trajectories and enhances their continuity.  
In the future, we plan to investigate RSSI fluctuation of iBeacon and improve its ranging accuracy. In addition, we will conduct more experiments in larger indoor spaces with sparse iBeacon configuration, and investigate the compensation to ranging error in longer distances. As smartphone can be held in different positions (horizontally or vertically in hand or in pocket), and these positions may lead to wrong altitude measurements for PDR, we will further develop the PDR algorithm to ensure accurate headings with different smartphone positions.  
Thus far, we have adopted little restriction for indoor positioning since we intend to present the improvement from raw measurements of iBeacons and smartphone sensors. Thus, specific human motion model could be introduced to predict the movement of pedestrians, and the transfer probability of different locations would be considered with map constraints. Other fusion approaches for indoor positioning will be investigated for real-time applications as well (e.g., grid-based filters). Finally, different data sources can be introduced for fusion such as geomagnetic data.  
Conceptualization, L.L. and B.L.; Methodology, L.L., B.L., and L.Y.; Supervision, B.L.; Writing--Original draft, L.L., B.L., and T.L.; and Writing--Review and editing, L.Y. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (41874030), The Scientific and Technological Innovation Plan from Shanghai Science and Technology Committee (18511101801), The National Key Research and Development Program of China (2016 YFB0501802), China Postdoctoral Science Foundation (grant number 2019M651581), and The Fundamental Research Funds for the Central Universities.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BLE & Bluetooth Low Energy \\ KF & Kalman filter \\ NLOS & non-line-of-sight \\ RSS & received signal strength \\ RSSI & received signal strength indication \\ RMSE & root mean squared error \\ PDR & pedestrian dead reckoning \\ CDF & cumulative distribution function \\ \end{tabular}