Continuous Train Positioning Using Visible Light Communication Assisted with Zero Velocity Update
Objective Accurate train location information is crucial for communications-based train control (CBTC) systems to ensure safe operation. With the current trend towards vehicle-to-vehicle communication and fully automatic operation (FAO) for subways, CBTC systems demand highly accurate and real-time...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Department of Journal of Sichuan University (Engineering Science Edition)
2024-11-01
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| Series: | 工程科学与技术 |
| Subjects: | |
| Online Access: | http://jsuese.scu.edu.cn/thesisDetails#10.12454/j.jsuese.202300658 |
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| Summary: | Objective Accurate train location information is crucial for communications-based train control (CBTC) systems to ensure safe operation. With the current trend towards vehicle-to-vehicle communication and fully automatic operation (FAO) for subways, CBTC systems demand highly accurate and real-time train positioning. Existing research into train positioning using visible light communication (VLC) achieves train location information by calculating the positional relationship between the receiving and sending ends. This attracts attention when a train is stationary or running at a uniform speed. However, little consideration has been paid to the accumulated error from the inertial measurement unit (IMU) and the zero-velocity offset of the IMU. The zero velocity update (ZUPT) algorithm can correct IMU errors by detecting the vehicle’s motion state and attitude angle to improve the accuracy and stability of positioning. The algorithm is simple, easy to implement, and has many applications in vehicle inertial navigation, robot positioning, human motion recognition, and other fields. This study proposes a continuous train positioning method based on VLC and ZUPT to optimize the results of dynamic train positioning and correct the offset from the IMU when a train is pulled over at a station, enabling seamless train positioning with high accuracy along an entire subway line. The proposed method can improve the accuracy of train positioning in tunnel environments and provide a reference for autonomous train positioning of CBTC systems toward vehicle-to-vehicle communication.Methods This study uses the LED light sources on the tunnel wall as the sending ends of VLC, while the binocular complementary metal-oxide-semiconductor (CMOS) camera fixed on the top carrier platform of the train head serves as the receiving end. This setup enables seamless train positioning with high accuracy along the entire subway line. First, based on the principle of VLC, the binocular CMOS camera at the head of the train captures images of the LED lamps, and the train simultaneously receives the corresponding strip information of the LED lamps using the blinking frequency information. Then, combined with IMU data, mechanical vibration at the receiving end is compensated to obtain the results of dynamic train positioning. In addition, to address the errors caused by train movement and when it is stationary, the train operation process over the entire line is divided into moving and zero-velocity sections. A zero-velocity detection condition is adopted to determine whether the train is stationary, and the results of dynamic train positioning are further optimized using the Unscented Kalman Filter (UKF) algorithm. Finally, the zero-velocity offset from the IMU is corrected to compensate for the divergence in the position and attitude estimation accuracy of the receiving end on the top of the train, achieving continuous high-precision positioning in the tunnel environment. The actual data from train operations on Chengdu Metro Line 1 are utilized to verify the feasibility of the proposed method. An experimental platform for autonomous train positioning, combining binocular stereo vision and VLC, is established, and MATLAB software is employed to analyze the experimental results of continuous train positioning.Results and Discussions A test point of train positioning is set every 0.5 m along the direction of train movement, and experiments are conducted 20 times on the optimization of train positioning in each positioning unit. When the train is running at speeds of 40, 60, and 80 km/h, the maximum error of train positioning after optimization with the unscented Kalman filter (UKF) is 15.27, 16.93, and 19.57 cm, respectively, and the accuracy of train positioning improves by 53.06%, 48.46%, and 52.71%, respectively (Fig. 5). After optimization with the UKF, the maximum error of train positioning during motion with constant acceleration is 18.48 cm, and during motion with constant deceleration, it is 18.24 cm (Fig. 6). The error of train positioning does not entirely change with the increase or decrease in train speed and still maintained a shrinking trend as the train approached the LED lamps (Fig. 7). When the train enters station B during motion with constant deceleration and left station B during motion with constant acceleration, the average error in a unit of train positioning using VLC is reduced to 6.52 cm (Fig. 8), and the error of train positioning at the stopping point is decreased to 15.18 cm (Fig. 9) after optimization with ZUPT-UKF. The proposed method meets the requirements of the IEEE 1474.1-2004 standard for train positioning and ensures continuous high-precision positioning throughout the entire line.Conclusion This study provides a train positioning method adopting VLC and the ZUPT algorithm to optimize the results of train positioning in the moving section and address the IMU’s accumulated error during stopping time in a tunnel environment. The experimental results showed that the maximum error of train positioning is 15.27, 16.93, and 19.57 cm, respectively, when the train runs in a straight line at constant speeds of 40, 60, and 80 km/h after UKF optimization. When the maximum target speed is 80 km/h, the maximum errors corresponding to constant acceleration and deceleration are 18.48 and 18.24 cm, respectively. After UKF optimization in the zero-speed section, the average train positioning error decreases from 18.74 to 6.52 cm, and the IMU’s accumulated error significantly declines. After ZUPT-UKF optimization, the average train positioning error reduces from 11.35 to 6.05 cm, and the train positioning error at the stop point diminishes from 30.55 to 15.18 cm, effectively suppressing the IMU’s zero-speed displacement. The proposed method meets the requirements of CBTC systems oriented toward vehicle-to-vehicle communication for the high accuracy of train positioning and ensures continuous high-precision train positioning throughout the entire line. |
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| ISSN: | 2096-3246 |