Using iBeacons for trajectory initialization and calibration in foot-mounted inertial pedestrian positioning systems

In foot-mounted positioning systems, it is hard to align multi-agent trajectories. In addition, the positioning accuracy is hard to maintain due to inertial drifts. An approach for trajectory initialization and calibration using iBeacons is proposed in this paper. This approach is under the framework of a particle filter. In the observation model of the particle filter, a nonparametric Gaussian Process (GP) regression model is adopted to describe the relationship between the estimated range and the observed RSS. Then the weights of the particles are updated according to the trained GP. GP is adopted here because it not only considers the sensor noise, but also the uncertainty in the model, which denotes the multi-path effects, human sheltering effects and so on in receiving the iBeacon signals. At last, a large-scale real-scenario experiment is carried out with a total walking length of about 5.4 kilometers. The results have demonstrated the effectiveness of the proposed approach for trajectory initialization and calibration, with the final positioning error reduced from 85.4 meters to only less than 1meter.