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Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion has been a major factor limiting the improvement in ultra-wideband (UWB) positioning accuracy, and the optimization of NLOS error has not yet been studied in a targeted manner. To this end, this paper deeply analyzes indoor scenes, divides NLOS into two forms of spatial occlusion and human occlusion, and proposes a particle filtering algorithm based on LOS/NLOS mapping and NLOS error optimization. This algorithm is targeted to optimize the influence of two different forms of NLOS, using spatial a priori information to accurately judge the LOS/NLOS situation of the anchor, optimizing the NLOS anchor ranging using IMU to project the virtual position, judging whether the LOS anchor is affected by human occlusion, and correcting the affected LOS anchor using the established human occlusion error model. Through experimental verification, the algorithm can effectively suppress two different NLOS errors of spatial structure and human occlusion and can achieve continuous and reliable high-precision positioning and tracking in complex indoor environments.