The present invention discloses a method for eliminating the near-far effect in a broadcast radio
positioning system, a storage medium, and a terminal device, comprising the following steps: multi-
source data acquisition and preprocessing; constructing a dynamic graph
data structure; constructing a
Transformer-GNN joint model; model training and optimization; and model evaluation and
adaptive optimization. The present invention implements global
signal correlation analysis through the self-attention mechanism of the
Transformer. Based on the alternating stacked GNN-
Transformer architecture, it introduces residual connections and a gating mechanism to dynamically fuse local-global features. It combines phased training with a dynamic
weight adjustment strategy to optimize positioning accuracy and interference suppression capabilities. It supports
multimodal data fusion, significantly improving anti-multipath performance and dynamic adaptability in complex scenarios, and solves the defects of traditional methods such as high computational complexity, strong hardware dependence, and insufficient environmental robustness. It is suitable for suppressing the near-far effect when satellites are unavailable, improving
system positioning accuracy, and enabling the
system to be better applied in high-precision positioning scenarios such as indoor positioning and
drone navigation.