Accurate train position information is crucial for communication-based train control (CBTC) systems to ensure the safety of train running. With the rapid development of vehicle-to-vehicle communication for CBTC systems, higher demands have been placed on positioning accuracy. When using optical camera communication for train positioning, the acquisition of positioning lamps directly affects positioning accuracy. Existing research has validated the feasibility and effectiveness of visible light communication (VLC) for positioning through various methods and has investigated camera rotation adjustments to stabilize target acquisition. However, limited attention has been given to the challenge of stably acquiring positioning lamps for fast-moving objects on tracks with different curve radii in tunnel scenarios. The extended state observer (ESO), as an intelligent observer, can dynamically adjust camera angles to maintain targets within the field of view, which demonstrates wide applications in autonomous driving, mobile robotics, and navigation. In the present study, we report an adaptive method for capturing lamps for train positioning using optical camera communication with ESO, ensuring stable lamp acquisition across tracks with varying curve radii. The proposed method enhances continuous positioning accuracy in tunnel environments.

In this study, the precise positioning of the train is achieved by adaptively adjusting the camera azimuth to continuously capture LED lamps on the tunnel wall. First, the influence of camera azimuth on acquiring train positioning lamps is analyzed to determine its critical conditions. Then, the current camera azimuth is measured using an inertial measurement unit (IMU) and optical flow sensor, while an ESO model for camera azimuth is constructed to estimate azimuth states. By calculating azimuth errors, disturbance information caused by external interference and unmodeled dynamics is estimated in real time. Then, a velocity-dependent adaptive control strategy for camera azimuth is designed using Lyapunov theory and adaptive backstepping, followed by dynamic surface control (DSC) approximation of virtual control derivatives to optimize response time and stabilize positioning lamp acquisition. Finally, the actual position of the train is calculated based on the coordinates of the positioning lamps, which enables continuous high-precision positioning in the tunnel environment. To verify the feasibility of the proposed method, we utilize line data and equipment information from Chengdu Metro Line 1 to establish an experimental platform for train positioning and use MATLAB to analyze the experimental results of positioning lamp acquisition and train positioning.

Using the distance between two LED lamps as one positioning unit, a train positioning experimental platform with dimensions of 20.0 m×2.0 m×1.5 m is established to validate the effectiveness of the proposed method. Under different curve radii, the maximum adjustment deviations of the camera azimuth are measured as 0.26°, 0.28°, 0.31°, and 0.34°, respectively (Fig. 8). The successful rate of capturing positioning lamps with a fixed azimuth is 79.98%, 71.89%, 62.40%, and 57.01%, while the successful rate with adaptive azimuth adjustment is 99.87%, 99.01%, 95.89%, and 93.95%, respectively (Fig. 9). When a train runs on a curve with a radius of 250 m at speeds of 20 km/h, 40 km/h, 60 km/h, 80 km/h, and 100 km/h, the successful rate of capturing positioning lamps is 92.29%, 91.84%, 91.63%, 91.48%, and 91.39%, respectively (Fig. 10). The maximum error in train positioning is 20.87, 22.35, 24.97, 27.84, and 30.04 cm, respectively (Fig. 11). When the train runs at a speed of 100 km/h, the maximum error of train positioning for the proposed method, traditional ESO, and the method without ESO is 30.04, 38.26, and 39.66 cm, respectively (Fig. 12). The maximum time for train positioning is 51.66, 60.56, and 67.64 ms (Table 2). The positioning accuracy and real-time performance of the proposed method both meet the train positioning requirements specified in IEEE Standard 1474.1-2025.

To address the issues of difficult acquisition of train positioning lamps due to curve radius limitations, we propose an ESO-based method for adaptively adjusting camera azimuth to reliably capture positioning lamps across varying curve radii. We utilize ESO to estimate camera azimuth disturbance and implement a train velocity-dependent adaptive control strategy for disturbance compensation, enabling adaptive azimuth adjustment. The research results show that while azimuth adjustment errors increase with decreasing curve radius, the process exhibits no significant overshoot or hysteresis. When a train runs at different speeds, the successful rate of capturing positioning lamps is higher than 91.00%, and the maximum error in train positioning is 30.04 cm. When a train runs at 100 km/h, the maximum time for train positioning is 51.66 ms. Compared with the traditional ESO method and the method without ESO, the positioning accuracy of the proposed method improves by 21.48% and 24.25%, respectively, when the curve radius is at its minimum. The proposed method enables continuous high-precision train positioning throughout the entire line.