Performance enhancement for INS/UWB integrated indoor tracking using distributed iterated extended Kalman filter

In this work, an improving distributed iterated extended Kalman filter (DIEKF) for INS/UWB integrated human positioning is proposed. In this model, we employ iterated extended Kalman filter (IEKF) as local filter for the data fuse in each wireless channel, and the distances measured by the UWB and that values measured by the IMU are both sent to the IEKF. Then, the main filter works with the output of the local IEKF and output the estimation of the INS position error. The optimal estimation of the navigation solution is calculated by the INS position and the filter output. The test results show that the performance of the proposed method is better than the traditional EKF in position accuracy.