Content area
For train control systems, the accuracy of positioning tracking is essential for ensuring the safety and efficiency of operations. Multi-source information fusion techniques can improve positioning accuracy, but the computational limitations of onboard equipment impede the real-time processing capabilities required by advanced information fusion algorithms. An innovative approach, which combines multi-sensor information fusion with edge computing, is proposed to reduce the computational load on onboard systems and accelerate data processing. Colored Petri Nets (CPNs) are utilized for the modeling and validation of the algorithm. State-space analysis is used to evaluate the functional safety of the proposed method. Numerical simulations are performed to identify the key factors affecting the train positioning method’s performance. These simulations also determine the minimal tracking interval required for effective operation under edge computing. The results show that the edge computing-based train fusion positioning method reduces data processing delays and improves positioning accuracy. This approach offers a practical solution for real-time and accurate train control systems.
Details
Petri nets;
Trains;
Data processing;
Collaboration;
Edge computing;
Vision systems;
Communication;
Modelling;
Sensors;
Onboard data processing;
Onboard equipment;
Algorithms;
Data integration;
Control systems;
Tracking;
Real time;
Monitoring systems;
Performance evaluation;
Data transmission;
Multisensor fusion;
Computer simulation
; Song, Haifeng 1
; Wu, Ruichao 1
; Zhou, Min 2
; Deng, Zixing 2 ; Dong, Hairong 3
1 School of Electronic Information Engineering, Beihang University, Beijing 100191, China;
2 School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China;
3 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;