A Real-time Indoor Tracking System in Smartphones

Abstract

The rapid growth area of ubiquitous applications and location-based services has made indoor localization an interesting topic for research. Some indoor localization solutions for smartphones exploit radio information and Inertial Measurement Units (IMUs), which are embedded in most of the modern smartphones. In this work, we propose to fuse WiFi Receiving Signal Strength Indicator (RSSI) readings, IMUs, and floor plan information in an enhanced particle filter to achieve high accuracy and stable performance in the tracking process. We provide an efficient double resampling method to mitigate errors caused by off-the-shelf IMUs and WiFi sensors embedded in commodity smartphones. The algorithms are designed in a terminal-based system, which consists of commercial smartphones and WiFi access points. We evaluate our system in two complex environments along moving paths. Experiment results show that our tracking method can achieve the average tracking error of $1.01$ meters and $90\%$ accuracy of $1.7$ meters.