## 1 Introduction  
With the rapid development of transportation networks, tunnels have become a crucial component of transportation infrastructure. Achieving high-precision two-dimensional positioning inside tunnels can not only enhance the operational efficiency of traffic within tunnels but also significantly improve the safety of autonomous vehicles and reduce the risk of traffic accidents. Current research and applications in indoor positioning primarily focus on the following technologies: Ultra-Wideband (UWB) positioning [1, 2], WiFi and Bluetooth positioning [3, 4, 5], Inertial Navigation System (INS) positioning [6, 7], ultrasonic and optical positioning [8], and positioning based on Time Difference of Arrival (TDoA) or Time of Arrival (ToA) methods [9]. Additionally, some researchers are employing GNSS relay methods to address indoor positioning challenges. Reference [10] investigates a novel GNSS relay architecture that downconverts GNSS signal frequencies to the 433 MHz ISM band and then upconverts them back to 1575.42 MHz to enable indoor positioning [10]. Reference [11] uses delayed repeaters for indoor GPS positioning by introducing specific delays into the received signals to distinguish signals from different repeaters. Kalmanfilter (KF) is then applied to fuse the results from two positioning methods to enhance accuracy [11]. Reference [12] investigates a multi-epoch differential indoor positioning method based on GNSS repeaters, which improves carrier phase measurement accuracy by introducing signal delays and multiple measurements, combined with the LAMBDA algorithm to resolve integer ambiguities. GNSS relay methods are increasingly emerging as a new trend in solving indoor positioning challenges [12]. In comparison to indoor environments, tunnels are typically narrow and linear spaces, where wireless signal propagation faces significant challenges due to severe multipath and reflection issues. Consequently, achieving two-dimensional positioning within tunnels remains one of the key technical challenges that urgently needs to be addressed [13]. In recent years, the issue of 2D positioning in tunnel environments has become a research focus. Reference [13] utilizes machine learning and mixed-integer convex optimization techniques to calculate the optimal landmark deployment locations, enabling unmanned vehicle navigation in environments without GNSS signals [13]. However, this method requires significant computational resources for real-world deployment, and uncertainties in landmark position estimation can impact positioning performance in complex environments. Reference [14] uses image bridging techniques combined with high-resolution cameras, LiDAR, and thermal imagers to estimate sensor position and pose through image sequences and geometric constraints, achieving positioning in tunnel environments [14]. However, the high computational complexity of this method affects real-time positioning, and the manual introduction of target points and geometric constraints during deployment increases complexity. Reference [15] employs magnetic field sensing technology, using magnetic fingerprint matching and dynamic time warping (DTW) algorithms to compare current magnetic field data with a fingerprint database to determine the vehicle position [15]. This method requires no additional infrastructure and is suitable for long-distance tunnels, but building a detailed magnetic field fingerprint database is necessary, and positioning accuracy can be affected by magnetic field variations. Reference [16] constructs virtual base stations and uses the direction of arrival (DOA) and ToA information from reflection paths, treating non-line-of-sight (NLOS) reflection paths as auxiliary line-of-sight paths to calculate vehicle position [16]. However, the algorithm's accuracy depends on the precision of the reflection paths, and positioning accuracy may be influenced by the tunnel environment.