Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Abstract

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.

Improving Indoor Positioning With Adaptive Noise Modeling

“…User dependent parameters are step length and radio receiver characteristics. Instead of inferring the step length from the height of a person, an improvement would be to use a model that estimates the step length from the walking dynamics, as in [29], [30]. To further improve the step and distance estimates, a possibility is to use a foot-mounted sensor, as in [31], where they achieved a distance estimation error of less than 1%.…”

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

“…The location of the tag device can be estimated based on the least squares system using x = (A T A) −1 A T b [136].…”

Possibilities of Using Kalman Filters in Indoor Localization

“…The Kalman filter can also be used as a tool to combine data of a different nature from different sources. This is used, for example, by Coronel et al [14], Lee et al [15], Chen et al [16], Chen et al [17], Yu et al [18], Liu et al [19] when dealing with a dynamic localization problem, they combine radio data and motion data from the respective mobile device sensors.…”