# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 1 Introduction  
Intelligent transportation systems rely on positioning services to ensure safe driving, including traffic condition awareness [1], accident detection [2], and vehicle tracking [3]. With the increasing construction of highway tunnels, tens of thousands of such tunnels exist worldwide, particularly in countries like China, Japan, Switzerland, and Norway. As of 2023, China has 7552 long tunnels totaling 13.21 million linear meters [4]. In these extended, enclosed environments, intelligent transportation systems depend heavily on precise positioning services to support critical functions such as safe driving, emergency response, and route planning. The development of global satellite technologies has significantly met outdoor positioning needs. However, in extreme, closed environments like tunnels, GNSS signals struggle to penetrate due to tunnel construction materials, structural designs, and terrain, making accurate positioning difficult. Meanwhile, indoor positioning technologies such as Wi-Fi [5, 6, 7], ultrawide band (UWB) [8, 9], Zigbee [10], long-term evolution (LTE) [11], and radio-frequency identification (RFID) [12] face challenges in achieving an ideal balance between scene versatility, deployment costs, and positioning accuracy. Pseudolite systems, which can both enhance the accuracy of satellite systems and provide independent positioning, represent an ideal positioning technology. Due to their highsignal power, strong anti-interference capabilities, wide coverage, and low deployment density, they are particularly well suited for continuously localizing high-speed vehicles in long tunnels. Typical pseudolite systems include Locata [13] and the Ground-Based High-Precision Local Positioning System (GH-LPS) [14]. However, in the typically closed environment of tunnels, positioning signals can experience significant spatial fading and multipath propagation [15], and conventional algorithms that only use intersections based on measured distances as the positioning results are complicated to map to the real environment, especially in curved tunnels, where the line of sight continuously changes, limiting visibility between the receiver and pseudolite [16], further complicating the localization process using conventional methods. The data learning capabilities of deep learning make it highly effective to handle high-dimensional, complex data, capture dynamic variations [17], and establish physical models between measured signals and spatial coordinates in real-world environments [18]. Various indoor positioning technologies have successfully employed deep learning to enhance accuracy [19; 20; 21]. However, research on leveraging deep learning to reduce positioning errors in pseudolite systems remains limited. Among the few existing studies, models validated in real-world environments have been primarily focused on small-scale scenarios [22; 23; 24]. Consequently, for long, enclosed tunnels spanning several kilometers, developing deep learning-based methods to enable pseudolite systems to deliver accurate and continuous positioning coordinates remains a critical area for further exploration. From the positioning perspective, achieving continuous localization requires integrating spatial and temporal elements [25]. Initially developed for language translation, the Transformer model [26] has been widely applied across various domains to learn temporal and spatial features, including image processing [27; 28], speech recognition [29], anomaly detection [30], and traffic flow prediction [31; 32]. Building on this foundation, we propose the Enconv1d model, designed to leverage the sequential characteristics of pseudolite pseudorange measurements. The model incorporates a temporal feature extraction module based on the encoder architecture from the Transformer, complemented by a multi-scale convolutional kernel 1D CNN module to enhance spatial feature learning. A dense layer integration module is then employed to constrain the positioning results from both spatial and temporal dimensions.