Indoor Tracking with Fusion of Wireless Positioning, Motion Recognition and Map Matching

Short-range wireless positioning and inertial measurement unit (IMU) have been widely used in indoor positioning systems. However, wireless signals fluctuate seriously and the accuracy of low-cost IMU system suffers from gyroscope drift, magnetic interference and accumulative error. In this paper, we propose a fusion positioning system based on Wireless signal, Map information and Inertial sensors, which is called as "WiMaln" system. The system has three key techniques, including motion recognition, particle filter and map matching. The motion state of the target is recognized from the IMU data, and then the system will choose different observations and particle transition models based on the detected motion state to get the final location estimation. Map matching is utilized to correct the heading error and constrain the weight updating of particles. Numerical experiments show that our proposed method could mitigate the accumulative error, obtain more stable performance, and achieve higher trajectory estimation accuracy than other existing methods.