Mitigating NLOS Interference in GNSS Single-Point Positioning Based on Dual Self-Attention Networks

The reception of nonline-of-sight (NLOS) signals in urban areas, such as urban canyons and overpasses, can cause severe errors in global navigation satellite system (GNSS) positioning. Machine learning-based NLOS mitigation methods have become increasingly popular. However, existing methods cannot obtain satisfactory NLOS recognition accuracy across multiple locations or scenarios. Furthermore, in scenarios with severe occlusion, directly removing recognized NLOS signals may reduce the number of available satellites for positioning algorithms, resulting in lower positioning precision. To address these issues, this study proposes a deep learning-based NLOS interference mitigation method to improve the precision of GNSS single-point positioning (SPP). First, to improve NLOS signal recognition across multiple locations, we propose the dual self-attention mechanism (DSN) model for NLOS recognition, which utilizes self-attention networks to construct both spatial and temporal channels for modeling spatial environmental characteristics and signal temporal features, respectively. Second, to mitigate the interference from NLOS signals, we design a novel weighting scheme using NLOS recognition results to revise the elevation angle-based scheme. Next, we propose the SPP-DSN algorithm by combining the DSN model and the designed weighting scheme to improve positioning precision in urban areas. Finally, we collected real-world data to conduct experiments to investigate the performance of our proposed method. The experimental results show that our proposed DSN model can effectively improve NLOS recognition accuracy across multiple locations. Compared to the regular SPP algorithm, our proposed SPP-DSN method can enhance positioning precision by over 29% in urban canyons and more than 10% under overpasses.