Edge Computing-Enabled Train Fusion Positioning: Modeling and Analysis

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Publicado en:Mathematics vol. 13, no. 6 (2025), p. 1015
Autor principal: Yin, Hao
Otros Autores: Song, Haifeng, Wu, Ruichao, Zhou, Min, Deng, Zixing, Dong, Hairong
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MDPI AG
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024 7 |a 10.3390/math13061015  |2 doi 
035 |a 3181569310 
045 2 |b d20250101  |b d20251231 
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100 1 |a Yin, Hao  |u School of Electronic Information Engineering, Beihang University, Beijing 100191, China; <email>sy2302512@buaa.edu.cn</email> (H.Y.); <email>sy2402409@buaa.edu.cn</email> (R.W.) 
245 1 |a Edge Computing-Enabled Train Fusion Positioning: Modeling and Analysis 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Accuracy 
653 |a Petri nets 
653 |a Trains 
653 |a Data processing 
653 |a Collaboration 
653 |a Edge computing 
653 |a Vision systems 
653 |a Communication 
653 |a Modelling 
653 |a Sensors 
653 |a Onboard data processing 
653 |a Onboard equipment 
653 |a Algorithms 
653 |a Data integration 
653 |a Control systems 
653 |a Tracking 
653 |a Real time 
653 |a Monitoring systems 
653 |a Performance evaluation 
653 |a Data transmission 
653 |a Multisensor fusion 
653 |a Computer simulation 
700 1 |a Song, Haifeng  |u School of Electronic Information Engineering, Beihang University, Beijing 100191, China; <email>sy2302512@buaa.edu.cn</email> (H.Y.); <email>sy2402409@buaa.edu.cn</email> (R.W.) 
700 1 |a Wu, Ruichao  |u School of Electronic Information Engineering, Beihang University, Beijing 100191, China; <email>sy2302512@buaa.edu.cn</email> (H.Y.); <email>sy2402409@buaa.edu.cn</email> (R.W.) 
700 1 |a Zhou, Min  |u School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China; <email>zhmin@bjtu.edu.cn</email> (M.Z.); <email>19120204@bjtu.edu.cn</email> (Z.D.) 
700 1 |a Deng, Zixing  |u School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China; <email>zhmin@bjtu.edu.cn</email> (M.Z.); <email>19120204@bjtu.edu.cn</email> (Z.D.) 
700 1 |a Dong, Hairong  |u College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; <email>hrdong@bjtu.edu.cn</email> 
773 0 |t Mathematics  |g vol. 13, no. 6 (2025), p. 1015 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3181569310/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3181569310/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3181569310/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch