To mitigate the influence of nonline of sight (NLoS) errors in ultra-wideband (UWB) ranging, this study presents a method that utilizes a genetic algorithm backpropagation neural network (GA-BP) for error identification and optimization. This method effectively detects and rectifies ranging errors and system deviations occurring in the NLoS propagation link, and subsequently improves the ranging outcomes through the application of Kalman filtering (KF). On this basis, this paper proposes a weighted concentric circle clustering localization (WCCGT) method to address the problem of no intersection or multiple intersection points in multilateral positioning caused by ranging errors. The method solves the problem of no intersection points through weighted concentric circle generation (WCCG). Then, it uses the mean shift clustering localization method to achieve a localization solution and improve localization accuracy. The experimental results show that the improved ranging optimization method effectively reduces the ranging error in the NLoS propagation link, and the ranging accuracy based on UWB is improved by more than 60%. Analyze through static positioning experiments and dynamic experiments, the positioning results of the WCCGT method were compared with the least squares (LS) method. The proposed method can achieve a positioning accuracy of 10.78 cm in NLoS environments, and the positioning performance has been improved by 17.32%.