A UWB Underground Mine Positioning Algorithm Based on Graph Neural Network

Ultra-Wideband (UWB) positioning is a promising technique for underground mine localization, but its accuracy is strongly affected by complex tunnel topology, multipath propagation, and non-line-of-sight (NLOS) ranging errors. To address these challenges, this paper proposes a three-dimensional UWB positioning algorithm based on a Multi-head Attention Feature Fusion Graph Neural Network (MAFF-GNN). The localization problem is formulated as a graph-based node position regression task, where anchors and tags are represented as nodes and UWB ranging links are represented as edges. The proposed model integrates graph message passing, multi-head attention-based feature fusion, global skip connections, and geometry-constrained regularization to learn reliability-aware spatial representations from noisy ranging measurements. A physics-guided simulated underground mine environment is constructed by considering tunnel geometry, wall roughness, coal dust concentration, humidity attenuation, and controlled NLOS conditions. Three mine-like corridor topologies are generated, with 10,500 localization samples in total. Experimental results under controlled simulation conditions show that MAFF-GNN achieves an RMSE of 0.323 ± 0.093 m, an MAE of 0.274 ± 0.024 m, and a P90 error of 0.480 ± 0.038 m. Compared with weighted least squares and support vector regression, the proposed method reduces RMSE by 74.16% and 40.30%, respectively. Robustness tests under different simulated NLOS ratios further indicate that the proposed graph-attention framework maintains a gradual error-growth trend as NLOS severity increases. These results indicate the potential of attention-enhanced graph learning for UWB localization in a challenging mine-like environment.