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Hazard Rate Estimation for GNSS-Based train localization Using Model-Based Approach

Chinese Journal of Electronics 2020年第1期29卷 49-56页

作者： LU Debiao TANG Dezhang SPIEGEL Dirk Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation School of Electronic and Information Engineering Beijing Jiaotong University Deutsche Bahn AG Bahntower

GNSS(Global navigation satellite system)has shown its ability on the non-safety relevant applications in various applications. In railway domain, safety is the utmost concern, GNSS-based train localization unit is the carrier of safe localization. EN 50126 standard specifies the electric and electronic system performance requirements as Reliability, availability, maintainability and safety(RAMS). Safety performance characteristic is based on the reliability and availability analysis *** formal method considering the risk identification and analysis is compulsory for the safety assessment. This paper provides the method for hazard analysis of the GNSS-based train localization function and further the hazard rate estimation of each hazard. The model-based approach using GNSS localization unit working principle is proposed to study railway localization behavior *** hazard for the localization unit is formally stated, real collected data on circular test track in Beijing is studied using the proposed method. The result shows that the safety requirement of the performance can be transformed into safety integrity properly, the risks of GNSS in train station environment conditions are presented.

关键词： Tolerable hazard rate GNSS Risk analysis train localization

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Multi-Hypothesis GNSS Error Estimation for train localization Safety Boundary Generation

Multi-Hypothesis GNSS Error Estimation for Train Localizatio...

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第32届中国过程控制会议(CPCC2021)

作者： Debiao Lu Baigen Cai Dezhang Tang Jian Wang Jiang Liu Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transport School of Electronic and Information EngineeringBeijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University

The railway signalling system requires accurate and dependable train location perception for train operation and *** locations bounded with accuracy levels are the inputs for the train operation *** uncertainty quantification quality brings possibility for safety assessment of GNSS-based train control *** paper presents a localization uncertainty estimation method,the location uncertainties are used to generate safety boundary of the location together with environmental scenario parameters and *** multi-hypothesis method considering the historical location data in the specific environment to generate the fitting error distribution is applied as the basis for measurement uncertainty estimation,with the inherent error,the hypothesis testing results and the horizontal protection level estimation using the acceptable integrity risk as the main parameter,the method is validated using experiment data collected at the Beijing-Shenyang high speed railway *** both satellite reception good and bad environmental scenarios,compared to the sensor fusion results provided by the reference system,the safety boundary in both scenarios is consistent to the error propagation.

关键词： GNSS train localization Safety Boundary Environmental Scenario

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Integrating GNSS and INS Data with Deep Learning-Enhanced Invariant Extended Kalman Filter for train localization

Integrating GNSS and INS Data with Deep Learning-Enhanced In...

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第43届中国控制会议

作者： Zheyu Sun Haifeng Song Min Zhou Hao Yin Hairong Dong School of Automation and Intelligence Beijing Jiaotong University School of Electronic and Information Engineering Beihang University College of Electrical Engineering and Automation Shandong University of Science and Technology

Ensuring precise train localization is crucial for the operational safety and efficiency of railway networks. Conventional localization techniques face limitations due to dynamic environmental noise, impacting the accuracy of such systems. In response, this study proposes a sophisticated approach that incorporates a deep learning-enhanced Invariant Extended Kalman Filter, utilizing a Convolutional Neural Network-Long Short-Term Memory(CNN-LSTM) architecture. This innovative method dynamically adjusts the noise parameters within the Kalman filtering process. The CNN component extracts spatial features,while the LSTM module analyzes temporal sequences, thereby optimizing the filter's performance under varying environmental conditions. The integration of Global Navigation Satellite System(GNSS) and Inertial Navigation System(INS) data forms the core of our investigation, addressing the complexities inherent in train localization tasks. Rigorous evaluations on real-world datasets have demonstrated a notable enhancement in localization accuracy, achieving a [specific improvement percentage] over conventional Kalman filter methods. The outcomes of this research underscore the transformative impact of deep learning on railway system advancements and set a foundation for subsequent innovations in intelligent transportation systems. By merging advanced deep learning algorithms with traditional signal processing, our work contributes to the evolution of more dependable and efficient train localization solutions, paving the way for future developments in this critical area of intelligent transportation systems.

关键词： Invariant Extended Kalman Filter Deep Learning Optimization train localization GNSS/INS Data Fusion

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Survey of Performance Evaluation Standardization and Research Methods on GNSS-Based localization for Railways

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Chinese Journal of Electronics 2020年第1期29卷 22-33页

作者： CAI Baigen WU Boqian LU Debiao Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation School of Electronic and Information Engineering Beijing Jiaotong University

Global navigation satellite systems(GNSS) has been regarded as a key technology for train localization in the future, as GNSS-based localization can reduce the deployment of wayside equipment, and offer a high-accuracy determination in a cost effective ***, the standardization of GNSS-based localization performance characteristics has not been done in *** can restrict the development of GNSS for safety related applications, which need critical requirement for the. The main objective of this paper is to provide a survey of research dealing with performance evaluation standardization. train localization technique and performance requirement in Chinese train control system(CTCS), GNSS performance characteristics suggested in standards and guidelines, characteristics of accuracy/integrity/reliability/availability/safety for GNSS-based train localization and related evaluation approaches are presented. Some issues in GNSS-based train localization are highlighted based on the survey.

关键词： Railway GNSS Performance evaluation train localization Safety

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