Kalman filtering is a common noise reduction method in the process of positioning using ultra-wideband (UWB). However, this algorithm has poor filtering performance of nonlinear system. The moving track of positioning target is easy to exceed the layout area of base station and be disturbed by abnormal noise, which will affect the accuracy and stability of positioning system. In order to solve this problem, a symmetric strong tracking ( SST) square root volume Kalman ( SCKF) algorithm was proposed. By introducing a symmetric time-varying fading factor to adjust each covariance matrix, the working mode of the multiple fading factor matrix in the error covariance matrix is changed, and then the filter gain is adjusted. Although the computational complexity increases slightly, the adaptability and robustness of the positioning model are enhanced. Simulation results show that under the interference of abnormal noise, the improved algorithm (SST-SCKF) can effectively improve the positioning accuracy compared with SCKF/ SCKF for multiple fading factors ( ST-ASCKF) algorithm, and the positioning trajectory is smoother than SCKF with single fading factor ( STSCKF). The positioning scheme based on UWB technology was designed by SST-SCKF algorithm. The dynamic simulation experiments show that the SST-SCKF algorithm proposed in this paper has better filtering performance than SCKF/ STSCKF/ ST-ASCKF. This SST-SCKT algorithm provides better noise reduction for personnel UWB positioning under complex environmental noise and makes the positioning more accurate.