An indoor dynamic positioning algorithm based on UWB and inertial measurement unit (IMU) fusion is proposed to deal with the problem that ultra-wideband (UWB) positioning is susceptible to various noises and nonlineofsight (NLOS). The algorithm firstly uses the extended Kalman filtering algorithm to filter the position information based on the angle of arrival (AOA) positioning method, and synchronizes the time with IMU data. By comparing the change speed of UWB position information at adjacent times with the movement speed of tags measured by IMU, the algorithm realizes the recognition and compensation of NLOS data, thus reducing the impact of NLOS on positioning accuracy. Then the improved particle filtering algorithm is used to optimally estimate the fused data to suppress noise interference and finally achieve accurate label location. The experimental results show that the proposed algorithm using AOA based location method can save the hardware cost while ensuring the location accuracy. Compared with the positioning scheme using only UWB sensors, the proposed algorithm can effectively reduce the positioning error of UWB according to the prior information provided by IMU, and has high reliability in the nonline of sight environment. Contrary to the fusion algorithm based on extended Kalman filter and unscented Kalman filter, the positioning accuracy is improved by 656% and 560% respectively. In contrast to the standard particle filtering algorithm, the running time of the proposed algorithm based on the improved particle filtering algorithm is reduced by 423%.