关键词: 多系统GNSS定位, Helmert方差分量估计, 可微架构, 深度学习, 自适应定权

Abstract: With the development of Global Navigation Satellite System (GNSS) technology, multi-constellation fusion has greatly optimized the spatial geometric configuration and increased the number of available satellites, improving the perfor-mance of GNSS positioning services. However, in complex urban environments, GNSS signals are highly susceptible to interference from non-line-of-sight (NLOS) signals and multipath effects, making it difficult for traditional stochastic models to accurately characterize the distribution of actual observation noise. This paper proposes a multi-system GNSS stochastic model refinement method based on a differentiable state estimation architecture. By combining the differentiable neural network NetW with the Helmert Variance Component Estimation (HVCE) method (termed NetW-HVCE), the proposed method effectively fuses the observation-level feature information of satellites extracted by the neural network and the multi-GNSS system-level weight information optimized by HVCE, and realizes the multi-level refinement of the GNSS stochastic model. Vehicle-mounted dynamic experiments in complex urban environments veri-fy that the proposed method achieves improved positioning performance. The root mean square error (RMSE) of three-dimensional positioning is reduced by 64.2% compared with the traditional Standard Point Positioning (SPP) method and by 32.1% compared with the existing deep learning-assisted positioning method SPP-TDL.

Key words: multi-system GNSS positioning, Helmert variance component estimation, differentiable architecture, deep learning, adaptive weighting