Indoor Positioning Based on Walking-Surveyed Wi-Fi Fingerprint and Corner Reference Trajectory-Geomagnetic Database
Yuan Wu, Ruizhi Chen, Wei Li, Yue Yu, Haitao Zhou, Ke Yan
Abstract
Indoor positioning has attracted many attentions in recent years. Wi-Fi, geomagnetic and inertial sensors are commonly used sources to reconstruct pedestrian's trajectory. But the error drift of inertial sensors and the labor-intensive fingerprints construction constrain their popularity. The purpose of this paper is to design a Wi-Fi, geomagnetic and pedestrian dead-reckoning (PDR) composed indoor positioning system which is accurate, practical and less labor-intensive. In order to achieve this goal, we design an indoor positioning system based on walking-surveyed Wi-Fi fingerprint and corner reference trajectory-geomagnetic database (CRTDB). Firstly, we propose a trajectory optimization algorithm which makes use of landmark observations to optimize historical positions of PDR by means of Gauss-Newton algorithm. Next, we construct the walking-surveyed Wi-Fi fingerprint database and the CRTDB using the optimized PDR trajectories. Secondly, we propose a Dynamic Time Warping (DTW) and Pearson Correlation Coefficient (PCC) combined CRTDB matching algorithm to find the coordinates of the observed corners. Thirdly, we propose a PDR, Wi-Fi fingerprint and CRTDB fused positioning system based on Kalman filtering (KF). Finally, we evaluate the performance and effectiveness of our proposed algorithms on two open datasets. The experimental results show that with our proposed CRTDB optimizing, the median error is improved by more than 33% and 27% respectively compared with that of PDR and Wi-Fi fusion.