A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

Aiming at the problems of high time overhead, low positioning accuracy, and inability to meet the requirements of indoor positioning applications in WiFi and Pedestrian Dead Rockoning (PDR) positioning technologies, a cross-layer positioning method based on multi-sensor and WiFi fingerprint fusion is proposed. Firstly, Multi-dimensional scaling technology (MDS) is used in WiFi location, which reduces the overhead of offline stage of large area WiFi fingerprint location and improves the responsiveness of online location. Without limiting the holding mode, the high and low threshold detection method is used to reduce the error in gait detection of the PDR method. Unscented Kalman Filter (UKF) is used to fusion WiFi and PDR positioning results, output the optimal two-dimensional estimation, and finally use WiFi signal and multi-sensor information to determine the height of pedestrian position and output three-dimensional coordinates. The experiment results show that the proposed method have higher accuracy and better stability compared with WiFi and PDR positioning method.