# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 2 Related Works  
As experiments in actual tunnels require the deployment of extensive infrastructure, part of the research data are derived from simulation experiments [33; 34; 35; 36]. In real-worldscenarios, ref. [37] applied LTE to tunnel positioning by constructing an LTE fingerprint database, determining the vehicle's location with the highest correlation to the LTE RSSI sequence from the user buffer, achieving an average positioning error of 44 m. Ref. [38] introduced a novel downlink time difference of arrival (DL-TDoA) technique, achieving sub-meter accuracy within a 150 m range. In a 400 m deep tunnel, the WiFi-based OwlPS model [39] reduced the average positioning error to 20 m. For intelligent transportation systems, however, positioning accuracy in tunnels needs to remain consistently within 0.5-1 m while providing continuous localization outputs [40; 41]. To meet the unique demands of tunnel environments, we refocus on the fundamentals of positioning. Positioning is finding the coordinates of an object in space at a given moment in time, which can be derived by calculating relative distances using specific reference points. (e.g., satellites in GNSS, RP points in WiFi-based systems) [42]. Continuous positioning further constrains these results over consecutive time steps. Consequently, achieving reliable continuous tunnel localization requires incorporating both spatial and temporal elements. Common positioning algorithms often focus on either the spatial dimension (e.g., RSSI [43; 44], AOA [45], CSI [46; 47]) or the temporal dimension (e.g., PDR [48], Kalman Filter [49], HMM [50]) [51; 52] to process observational signals. Deep learning has been extensively employed to model positioning signals from both spatial and temporal perspectives, significantly improving localization accuracy. For example, ref. [53] proposed a Siamese network to learn the implicit spatial correlations in CSI, ref. [54] designed a spatial fingerprint attention encoder as a spatial feature extractor, and ref. [55] introduced the LF-DLSTM algorithm to encode temporal dependencies using local features. With the advancement of deep learning, combining spatial and temporal features for precise positioning has become a prominent research direction. For instance, ref. [56] leveraged UWB to propose a model incorporating convolution and LSTM modules to extract spatio-temporal features of positioning errors. Ref. [57] introduced the FSTNet deep learning framework, which uses convolution and attention mechanisms to capture spatio-temporal correlations from fingerprints. Currently, the modeling capabilities of deep learning have been applied to pseudolite positioning systems. For instance, ref. [22] incorporated simulated fingerprint features generated from the carrier-to-noise ratio and carrier phase difference into the input of a neural network, achieving decimeter-level accuracy based on PLS in a 12 m \(\times\) 12 m airport hall. Ref. [23] proposed the NLOS and Multipath Detecting Network (NMDN) to analyze the impact of NLOS and multipath effects on ranging within PLS data. Additionally, ref. [24] utilized a variational autoencoder (VAE) with location information to learn the probabilistic distribution features of pseudolite carrier phase differences in latent space, enabling feature clustering and achieving decimeter-level accuracy in a 7 m \(\times\) 10 m office environment. The existing literature suggests that research on optimizing pseudolite positioning algorithms using deep learning remains in its early stages, with only a few studies demonstrating the potential of deep learning to enhance pseudolite positioning performance in specific scenarios. However, there has been no exploration of combining deep learning with pseudolite systems to address the localization challenges in long-distance tunnel environments. Expanding the application of deep learning to pseudolite positioning systems, known for their strong interference resistance and wide coverage, represents a critical direction for future technological development.