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4th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Shang, Yaobo Li, Runling Zhang, Jiaqiang Zhang, Dongwei Li, Zhe Air Force Engineering University Air Traffic Control and Navigation College Xi'an China Shaanxi Key Laboratory of Meta-Synthesis for Electronic and Information System Xi'an China The Fourth Military Representative Office of the Air Force in Beijing Beijing China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

A frame synchronization algorithm based on adaptive double-threshold decision is proposed for the communication system using pseudo-random code as frame synchronization code. Firstly, the correlation output result is subjected to moving average operation and an adaptive threshold is generated through parameter adjustment so as to complete the correlation peak amplitude judgment;Then, according to the channel redundancy judgment idea, multiple verification judgments are carried out by utilizing the position information of the relevant peaks, and when the number of the relevant peaks with the position information conforming to the verification rules reaches a set threshold value, the judgment capture is successful;Finally, the synchronization ambiguity caused by multiple decisions is eliminated by inserting the flag information. Theoretical analysis and numerical simulation results show that the performance of the proposed algorithm is significantly improved in terms of system detection probability and false alarm probability compared with single decision. © 2022 IEEE.

关键词： Signal detection

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Trajectory Prediction of High-Speed Train Group Tracking Based on LSTM-KF Hybrid Model

Trajectory Prediction of High-Speed Train Group Tracking Bas...

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International Conference on Intelligent Transportation

作者： Boyu Yang Jidong Lv Hongjie Liu Ming Chai Qinglong Zhang Tao Tang Jiahui Lv National Engineering Research Center Of Rail Transportation Operation And Control System Beijing Jiaotong University Beijing China School of Civil and Resource Engineering University of Science and Technology Beijing China Beijing Jiaotong University and the Associate Director of the Rail Traffic Control and Safety State Key Laboratory

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Operating multiple trains as a group under Train-to-Train communication is an efficient way in sparse railway networks. However, it is a barrier to accurate tracking for follower trains with the uncertain status of the preceding trains under communication delay. Therefore, we proposed a train trajectory prediction approach, aiming to obtain an accurate state of preceeding trains in a long time horizon. Firstly, we established a hybrid prediction model combined Long Short-Term Memory (LSTM) with the Kalman Filter (KF) and using the sliding window mechanism to enhance future prediction accuracy. Secondly, to fusion LSTM prediction data and the Kalman calculation model, we proposed the Hybrid Train Trajectory Prediction algorithm by adopting real-time calculation of the covariance matrix using real data and predicted data. Finally, we apply an experiment considering network characteristics in Western China. The result shows that the Mean Absolute Error (MAE) of series prediction in the longer time domain is 44% of the Recurrent Neural Network (RNN) model and 88% of the traditional LSTM model.

关键词： Recurrent neural networks Accuracy Predictive models Prediction algorithms Real-time systems Rail transportation Data models Trajectory Kalman filters Long short term memory

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A Space-Time Interval Based Safety Protection Model for Virtual Coupling Heavy-Haul Trains 27

A Space-Time Interval Based Safety Protection Model for Virt...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Xinyi Chai, Ming Wang, Qi Su, Haoxiang Wang, Hai-Feng Lv, Jidong Zhang, Yong National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing100044 China Standards and Metrology Research Institute China Academy of Railway Sciences Limited Corporation Beijing100081 China Crsc Research and Design Institute Group Co. Ltd. Beijing China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331505929

Heavy-haul railway is one of the most cost-effective ways of transporting bulk goods globally, and enhancing its capacity is critical. The dominant method of capacity enhancement is increasing the load capacity of a single train, carrying risks of accidents. Introducing virtual coupling into heavy-haul railways is expected to solve these problems and significantly increase capacity. However, the absolute braking distance based safety protection model cannot meet the demand for distance between adjacent virtually coupled heavy-haul trains. To overcome this shortage, we propose a space-time interval based safety protection model for virtual coupling heavy-haul trains. Constraints are added to the model to ensure the two trains will not collide during the entire braking process. Aiming at the feature of long braking delay caused by the extended length of heavy-haul trains, we develop a braking wave propagation model to establish a more accurate safety braking model and shorten the safe distance between adjacent virtually coupled trains. Different safety braking models are used for the preceding and following trains to improve computational efficiency. The experimental results show that the safe distance of this model is shortened by 41.8% compared with the ABD based safety protection model at 80 km/h. © 2024 IEEE.

关键词： Freight cars

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A Model Predictive control Approach for Virtually Coupled Train Set Based on Multi-Phase Trajectory Planning

A Model Predictive Control Approach for Virtually Coupled Tr...

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IEEE International Conference on Networking, Sensing and control

作者： Bosi Li Hongjie Liu Yinghui Lang Qingdong Jia Kun Zhang Kaixuan Li School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Traffic Control Technology Co. Ltd. Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Virtual coupling (VC) is an emerging technology that enhances railway operations by dynamically and accurately matching line capacity with passenger demand. However, challenges arise in coordinating the virtually coupled train set (VCTS) during inter-station operations and maintaining accurate control of the VCTS. To address this issue, we propose a multi-phase trajectory planning control approach to generate the reference trajectories for VCTS to enhance the synchronicity of interstation operations. However, during speed-varying inter-station operations, it remains challenging to control VCTS accurately under disturbances while adhering to safety constraints. The control layer employs a model predictive control (MPC) approach to ensure precise reference tracking, based on the optimized reference trajectories for all train units. The effectiveness of this approach is validated through experimental results.

关键词： Accuracy Trajectory planning Trajectory tracking Predictive models Rail transportation Trajectory Safety Sensors Public transportation Predictive control

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Trajectory Prediction of High-Speed Train Based on GMM-LSTM

Trajectory Prediction of High-Speed Train Based on GMM-LSTM

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International Conference on Intelligent Transportation

作者： Wanqi Tian Bing Bu Jidong Lv Tao Tang Kaicheng Li National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

By using vehicle-to-vehicle (V2V) communication technology to interconnect trains while maintain a shorten distance under the premise of safety condition, is the development direction for improving the efficiency of high-speed trains. Trajectory prediction of train ahead is an important mean to further reduce the tracking distance. In this paper, based on Gaussian mixture model (GMM) and long short-term memory (LSTM) Recurrent Neural Network (RNN), we propose a personalized trajectory prediction method model for high-speed trains. The main idea is to achieve accurate and personalized trajectory prediction by recognizing the driving style of the train ahead to realize a shorter distance tracking control. Firstly, based on the GMM, three different driving styles are identified by combining the characteristic data of tracking trains, and the characteristic importance of driving styles are analyzed by MIC. Secondly, based on different driving styles, a novelty personalized trajectory prediction algorithm is worked out by modified LSTM-RNN models. Finally, experiments are carried out using the real data of the on-board equipment and ground control equipment. The results indicate that, compared with the traditional trajectory prediction methods, the proposed personalized trajectory prediction method shows significant advantages.

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An MPC-based cooperative control approach for separated power electric multiple units

An MPC-based cooperative control approach for separated powe...

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International Conference on Intelligent Transportation

作者： Zixuan Zhang Yuan Cao Shuai Su Junlin Zhang Wei Wang School of Electric and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety and the Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing China Traffic Control Technology Co. Ltd Beijing China Department of Automation Traffic Control Technology Co. Ltd Tsinghua University Beijing China

The integrated control of separated power electric multiple units (EMUs) and cars is a significant research field, the cars cooperative control problem of the separated power EMUs is investigated in this paper. Firstly, assuming that the separated power EMUs are composed of different numbers of powered cars and trailer cars connected by elastic couplers, and considering the electric traction/braking force, air braking force, and nonlinear basic resistance, a multi-particle model is established. Then a novel cost function is designed considering speed and position tracking errors, in-train force, traction energy consumption, and regenerative energy. Based on the model predictive control (MPC) methodology, a cooperative control approach is proposed to optimize the operation performance of separated power EMUs. The proposed control approach can improve EMUs tracking accuracy, and reduce internal force and net energy consumption during EMUs operation. Finally, the proposed approach is verified by numerical simulations.

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A Stochastic Reachable Set Trajectory Prediction Modeling Method for Following Operation of Urban Rail Trains

A Stochastic Reachable Set Trajectory Prediction Modeling Me...

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International Conference on Intelligent Transportation

作者： Min Yang Jidong Lv Zhengwei Luo Hongjie Liu Ming Chai Lei Zhang National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Traffic Control Technology Co. Ltd Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

Trajectory prediction of the preceding train is a vital problem in the following operation scenario of urban rail train. However, due to the existence of sensor error and communication delay, uncertainty of trains makes it great challenge to predict train trajectory accurately. To overcome the problem, a stochastic reachable set modeling method is proposed to predict the motion trajectory associated with additional probabilistic information of the preceding train, such as position and speed etc. The main idea is to achieve a complete representation of the uncertain behavior of the train, and to improve the computational efficiency by abstracting stochastic reachable sets into extended Markov chains. Two typical validation cases demonstrate the completeness of the proposed modeling method in representing train following behaviors, thus indicating its credibility in trajectory prediction.

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考虑跨服务模式的网约车派单优化

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Journal of Central South University 2023年第2期30卷 642-653页

作者：王印权吴建军孙会君张宇丰吕莹 State Key Laboratory of Rail Traffic Control and Safety(Beijing Jiaotong University) Beijing 100044China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport of Ministry of Transport(Beijing Jiaotong University) Beijing 100044China Institute of Transportation System Science and Engineering Beijing Jiaotong UniversityBeijing 100044China

为解决多服务模式的网约车市场中所存在的模式间供需时空不匹配的问题,打破模式间派单壁垒,减少车辆无效巡游,提升乘客体验,提出一种基于强化学习的跨模式派单算法。首先,设计跨模式派单的运行规则和相应的仿真环境为算法的训练提供环... 详细信息

为解决多服务模式的网约车市场中所存在的模式间供需时空不匹配的问题,打破模式间派单壁垒,减少车辆无效巡游,提升乘客体验,提出一种基于强化学习的跨模式派单算法。首先,设计跨模式派单的运行规则和相应的仿真环境为算法的训练提供环境支持。其次,针对派单过程中乘客需求动态变化的特点,设计两阶段派单流程。在第一阶段,根据车辆与需求的时空特征确定虚拟派单请求;在第二阶段,采用k最近邻算法对可行派单请求进行细化,选择与虚拟派单请求最为相似的请求作为最终的派单方案。实验表明,与基线方法相比,所提出的跨模式派单算法提升了基础服务模式中3.1%的请求响应率,同时减少了高级服务模式中车辆的空驶时间,实现了更高的经济效益与服务水平。

关键词：网约出行多服务模式跨模式派单强化学习新兴交通系统

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A Novel Modulation Recognition Algorithm for Satellite Communication and TT&C Signals

A Novel Modulation Recognition Algorithm for Satellite Commu...

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Signal Processing, Communications and engineering Management Conference (ISPCEM), International

作者： Dongwei Zhang Xiaolong Liang Canyu Huang Yaobo Shang Shaanxi Key Laboratory of Meta-Synthesis for Electronic and Information System Air Traffic Control and Navigation School of Air Force Engineering University Xi'an China Air Traffic Control and Navigation School of Air Force Engineering University Xi'an China

Electromagnetic signal modulation mode is one of the key elements of battlefield technical parameter information. This paper proposes an efficient modulation recognition algorithm for satellite communication and TT&C signals based on decision tree, which can accurately identify eight satellite modulation modes, including BPSK, QPSK, 2FSK, 4FSK, 16QAM, BPSK/PM, QPSK/PM and 2FSK/PM. The spectrum characteristics of the modulation methods commonly used in satellite communication and TT&C signals are firstly analyzed, and the main differences between and within categories of modulation methods of satellite signals are summarized; Then, rational feature parameters are selected and designed, and a complete modulation recognition process is proposed based on these parameters; Finally, the correctness and performance of the proposed recognition algorithm are evaluated by numerical simulation. The simulation results show that when the SNR (signal-to-noise ratio) of the proposed algorithm is more than 4dB, the modulation recognition accuracy of the eight satellite communication/TT&C signals can reach above 95%.

关键词： Satellites Simulation Modulation Signal processing algorithms Classification algorithms Binary phase shift keying Character recognition

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