In indoor environment, the Ultra-Wide Band (UWB) signal is often affected by the interference from the occluding objects, which leads to the anomaly of the measured distance data and spurious delay, resulting in the degradation of the positioning performance. Firstly, a K-means model is developed to reject the false observation values by preprocessing the Euclidean distance between data. Secondly, the conventional least-squares localization method is improved by using the L2 norm regularization method. Finally, in order to verify the effectiveness of the method, an application example of indoor 3D positioning of an unmanned aerial vehicle (UAV) is designed in this paper. The flight data of UAV are collected under the conditions of no interference and interference, and then the proposed data preprocessing method is applied to eliminate the abnormal data and estimate the position of UAV in both cases using the improved least squares method. The results of the practical example show the effectiveness of the proposed method, which can improve the UWB 3D positioning accuracy under the interference environment.