# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 5 Conclusions  
The development of intelligent transportation systems has heightened the demand for precise localization services, especially in complex environments such as tunnels. Traditional model-based positioning algorithms still face significant challenges in providing high-precision localization services. To address these challenges in highly enclosed environments like long tunnels, we explored an application-oriented localization algorithm that integrates deep learning with pseudolite systems. Leveraging the linear learning capability of deep learning for complex data, this study proposes the Enconv1d model for pseudolite-based localization in long tunnels. The Enconv1d model is based on the Transformer's encoder structure and employs a multi-head self-attention mechanism to learn long-term temporal dependencies and global spatial relationships. We incorporated multi-scale convolutional layers to enhance the encoder structure's ability to capture multi-scale, locally sensitive information while maintaining low layers. These layers combine global spatial information with local spatial details. From a structural perspective, the pseudorange measurement received by the user enters the temporal feature extraction module and spatial feature extraction module after the single difference calculation. Then, the two features are integrated by the Dense layer to output the positioning results. The temporal feature extraction module comprises \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **FLOPs (GFLOPS)** & **MACs (MMACs)** & **Params (M)** & **Time (ms)** \\ \hline Enconv1d & 1.47 & 734.7 & 11.28 & 0.065 \\ Conv1d & 0.24 & 115.17 & 1.74 & 0.029 \\ Encoder & 1.22 & 700.61 & 11.14 & 0.049 \\ LSTM & 0.09 & 1.06 & 0.14 & 0.032 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relevant parameters that reflect the model complexity and efficiency. an input layer (sliding window), two encoder layers, and an output layer. The spatial feature extraction module includes a local information module--composed of convolutional kernels of three different scales and two encoder layers--and a global information module consisting of two additional encoder layers. To validate the performance of the proposed model, we conducted tests on four different paths within a 4.6 km tunnel. The results demonstrate that the Enconv1d model achieves a minimum Mean Distance Error of 0.9 m, a minimum MAE of 0.75 m, and a minimum RMSE of 0.98 m. Furthermore, 80% of the localization errors are below 1.36 m, significantly outperforming the other models. Regarding the distribution of distance errors, the proportion of errors over 3 m for our model is consistently below 1.5%, while the proportion of errors less than 1 m is substantially higher than for other models. Additionally, the overall error distribution is more concentrated within the 0.5-1 m range, with a maximum error not exceeding 4 m. These findings indicate that the proposed model effectively limits outliers while enhancing overall localization accuracy. The multi-scale convolutional kernels contribute to learning deeper, locally sensitive features, complementing the encoder structure to optimize localization performance further. Our research provides new insights into optimizing pseudolite positioning algorithms and has demonstrated its feasibility through high-cost validation. The pre-deployment of the GH-LPS system in high-speed tunnels requires significant investment in hardware and infrastructure. Furthermore, the experimental environment is situated within a normally operating highway tunnel, and data collection was conducted during regular traffic flow, ensuring the authenticity of the data while adding complexity to the experiment. Given the continuous advancements in deep learning technology, we can further explore the integration of pseudolite and deep learning, applying it to a broader range of environments. This could offer robust positioning services for fields such as traffic management, navigation guidance, and autonomous driving.