IEKF-SWCS method for pedestrian self-navigation and location

In the process of using wearable inertial measurement unit to realize pedestrian navigation, accumulated drift errors are increasing with pedestrian moving, which has serious effects on the navigation accuracy. To solve this problem, a pedestrian self-navigation and location method was proposed based on improved extended kalman filter (IEKF). An 18 dimensional filter model fused with human motion characteristics was built. Meanwhile, a step wise closed loop smoothing (SWCS) algorithm was designed in IEKF, which could eliminate the sharp correction at some sample points and improve the smoothness of the trajectory. A self-developed IMU sensor was used to make tests. The results demonstrate that the proposed method can significantly restrain the divergence of MEMS IMU, and effectively improve the location accuracy. In the process, no extra hardware cost has produced. So this method has practical application value for pedestrian navigation.