Integrated iBeacon/PDR Indoor Positioning System Using Extended Kalman Filter

Indoor positioning is a challenging task in location-based services (LBS). The basic requirements of the indoor positioning system are high accuracy, availability, and cost and energy efficiency. Apple's Bluetooth Low Energy (BLE) based iBeacon along with pedestrian dead reckoning (PDR) system meets the aforementioned requirements. For iBeacon based indoor positioning, path-loss model is adopted to calculate the distance between user and iBeacon, and the maximum likelihood estimate positioning method is proposed for positioning. In the PDR positioning system, the Mahony Attitude and Heading Reference System (AHRS) is adopted to calculate the attitude of smart phone, in order to improve the performance of heading inference. Because of the presence of error accumulation over time in PDR based positioning system, along with the fact that the iBeacon based positioning system is susceptible to disturbance, we proposed an integration algorithm for iBeacon and PDR using Extended Kalman Filter (EKF). Experiments indicates that the proposed method can achieve a meter-level precision.