Abstract: In complex environments such as electric power operation scenes, the performance of ultra wideband (UWB) positioning is seriously degraded due to non line of sight (NLOS) scenarios. The integration of UWB and inertial measurement unit (IMU) can improve the positioning accuracy, but there is error accumulation in IMU measurement, which requires accurate UWB measurement correction. Accurate identification and utilization of NLOS conditions is helpful to improve the positioning accuracy. In this paper, a UWB/IMU fusion algorithm based on extended Kalman filter (EKF) is proposed, which uses the distribution of UWB measurements in electric power operation scenes to determine NLOS conditions and mitigate errors, thus effectively improving the positioning accuracy under NLOS conditions. The proposed algorithm has good usability since it does not need prior knowledge of the environment and IMU correction. Theoretical and practical experimental results show that the performance of the proposed algorithm is superior to other baseline systems.