新型 MEMS-IMU/Wi-Fi 组合室内行人定位算法

A novel integration strategy for accurately estimating pedestrian location has been developed by combining microelectromechanical systems-inertial measurement units (MEMS-IMUs) and Wi-Fi data. This approach enables the effective use of MEMS-IMU devices in smartphones. The research work included: establishing a position estimation model relative to the reference coordinate system based on the pedestrian dead reckoning algorithm; directly using the output of the gyroscope in the IMU as the original observation value, and adopting the Wi-Fi position data at a fixed frequency for system correction, then designing the extended Kalman filter algorithm to estimate the real-time position coordinates of pedestrians; creating the motion trajectory of pedestrians and verifying the effectiveness and feasibility of the algorithm by collecting the data of mobile phone sensors and wireless signal access point. The results show that when using integrated MEMS-IMU and Wi-Fi positioning, the position error becomes significantly smaller than that of IMU-only positioning. The findings also indicate that Wi-Fi information can assist MEMS inertial sensors to more accurately complete indoor pedestrian navigation and positioning tasks.