The Global Navigation Satellite System (GNSS) provides meter-level positioning in open environments, but its accuracy degrades severely in dense urban areas due to signal blockage and multipath effects. To address this problem, this paper proposes a hierarchical collaborative fusion positioning method based on GNSS, 5G, and the Inertial Navigation System (INS) with cross-source observation quality assessment. The proposed method integrates dual-domain error suppression, adaptive-shrinkage Unscented Kalman Filter (UKF) estimation, and observation-quality-aware adaptive weighting to mitigate systematic bias, random gross errors, and observation degradation. Unlike conventional fixed-weight or single-source-quality fusion schemes, the proposed method jointly combines gross-error detection, residual-driven covariance shrinkage, and adaptive weight regulation in a unified framework. Experiments were conducted in open outdoor, semi-occluded outdoor, and fully occluded indoor scenarios. The proposed method achieved a horizontal RMSE of 1.61 m in the semi-occluded outdoor environment. Compared with the the long short-term memory (LSTM)-aided UKF baseline, the positioning RMSE was reduced by 32.4%, and the positioning interruption rate was reduced by 49.5%.
Tang et al. (Mon,) studied this question.