Affiliations: [a] Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| [b]
Intel Penang, FIZ 3, Bayan Lepas, Pulau Pinang, Malaysia
| [c] Department of Software and Communications Engineering, Hongik University, Sejong, Korea
Correspondence:
[*]
Corresponding author. Seong Oun Hwang, Department of Software and Communications Engineering, Hongik University, Sejong, Korea. E-mail: sohwang@hongik.ac.kr.
Abstract: Existing smartphones have many built-in sensors which allow various indoor positioning techniques (IPS) to be developed without the need of additional hardware. Among the techniques, Pedestrian Dead Reckoning (PDR) is one of the more suitable ones for continouos positioning tracking. Through the accelerometer, magnetometer and gyroscope sensors, the number of steps and the direction of those taken steps can be captured and the position of the user can be inferred from a known starting point. Despite the PDR technique is simple, the implementation is not so straight forward as there are some challenges involved. Some of the notable challenges include i) the positioning error accumulates over time; ii) sensors from different phones models produce inconsistent measurement data; iii) walking gaits and step lengths vary greatly for different users. To overcome the error accumulation of PDR, many existing works use beacons to correct a user’s position estimation when the user is close to a beacon. Instead of using beacons, this work introduces the concept of correction points, which are logical points where their locations are known. Besides inferring the user’s position, the proposed solution also use the correction points to recalibrate user’s PDR step length and improves the overall positioning accuracy. From the experimental results, the proposed solution with correction points is able to perform continuous position tracking on three different smartphone models with more than 50% of accuracy improvement compared to IPS that only uses PDR alone. On top of that, we also show how the proposed system can be utilized to perform Wi-Fi fingerprint mapping, which can reduce the effort for system maintenance.
