Indoor Multi-Floor 3D Target Tracking Based on the Multi-Sensor Fusion

In recent years, indoor target tracking based on pedestrian dead reckoning (PDR) and the built-in inertial sensors of smartphone has become a research hotspot in location-based services (LBS). However, indoor 3D target tracking using smartphone inertial measurement unit (IMU) mainly face challenges of error accumulation caused by the heading drift of sensors and height fluctuation caused by the instability of barometer. This paper proposes a multi-floor 3D target tracking system based on the built-in inertial sensors of smartphone. We first establish a 2D PDR movement model by raw sensor data and then 2D PDR position is corrected in real time by rebuilding the particle filter model and refining the calculation method of particle weight to reduce the accumulated errors. A height displacement measurement method based on Kalman filter and floor change detection (FCD) algorithm is proposed to extend the 2D PDR tracking to 3D space. We first skillfully use Kalman filter to fuse the data of barometer and accelerometer, after that, the FCD algorithm is proposed to evaluate and modify the output height of the Kalman filter, evaluating the floor change state and maintaining steady height. The 3D target tracking, which consists of real-time 2D trajectory information and height information, is provided to the pedestrian. Experimental results demonstrate that the proposed method based on the fusion of various technologies can effectively maintain track stability and smoothness with low cost and high positioning accuracy. Moreover, additional peripheral devices need not be arranged in advance.