An improved NLOS error mitigation algorithm for 5G positioning in complex urban environments

Abstract Real-time and high-precision Fifth-generation mobile communication technology (5G) positioning is essential for establishing a wide-area and high-accuracy spatiotemporal reference framework in urban environments. However, a main challenge is the Non-Line-Of-Sight (NLOS) error significantly impact positioning accuracy, limiting the full deployment and application of 5G technology. In this study, a novel NLOS error mitigation method using Virtual Base-Station (VBS)-assisted algorithm is developed to enhance both kinematic and static positioning performance of 5G systems in complex urban environments. The proposed method consists of three modules: (1) a Time-Of-Arrival (TOA) positioning model, (2) a VBS generation method, and (3) a stable-state discrimination method. The TOA positioning model utilizes raw TOA measurements and a conventional four-station localization algorithm to estimate the location of user equipment. The VBS generation method optimizes Base-Station (BS) performance with particle filter combined with a random-distribution algorithm. The stable-state discrimination method employs the Augmented Dickey-Fuller (ADF) test to assess the stationarity of the feedback iteration process in VBS optimization. Several experiments are conducted in diverse scenario areas to evaluate the effectiveness, accuracy, and robustness of the proposed method. The results demonstrate that the proposed method significantly outperforms the traditional localization method, a 21.09% improvement in Three-Dimensional (3D) positioning accuracy. Compared to the state-of-the-art method, the proposed algorithm not only achieves slightly higher accuracy but, more importantly, reduces significantly the computation time.