An Indoor Positioning Approach Using Smartphone Based on PDR and EKF

Recently, Pedestrian Dead Reckoning (PDR) based methods, which perform mobile continuous indoor positioning, have obtained remarkable performance. However, the precision of the hardware commonly used is pretty low, many existing methods may produce large errors. In this paper, we propose a novel method, which uses low power Bluetooth Beacon as auxiliary sensor, to improve positioning accuracy and reduce Bluetooth deployment costs. To get more accurate steps, we use a filtering window to filter the acceleration feature. Then, we introduce an Extended Kalman Filter (EKF) method to correct PDR navigation. Furthermore, we reduce the number of Bluetooth Beacon by using a Cooperation-Proximity method. The experimental results show that the filtering method proposed in this paper can filter out invalid acceleration feature, so as to accurately measure the number of steps. In terms of the fitting degree between the walking track and the set route, the proposed method is 2.5% - 12% higher than the traditional methods, which reflects the improvement on positioning accuracy.