In indoor wireless positioning systems, non-line-of-sight (NLOS) propagation caused by base station deployment constraints and environmental structures poses a major challenge to positioning accuracy. Mitigating the adverse effects of NLOS propagation without increasing deployment cost remains a fundamental challenge in indoor wireless positioning. To address this challenge, we propose a novel correction framework that utilizing ranging information and structural constraints to construct virtual line-of-sight (LOS) base stations as substitutes for NLOS measurements, thereby enabling effective utilization of NLOS signals. The proposed method models the structural information of the indoor environment in a planar form and, in combination with a location prediction algorithm, enables accurate identification of signal types in dynamic scenarios. Furthermore, an NLOS reconstruction algorithm is developed to infer feasible propagation paths of identified NLOS signals, allowing reliable virtual LOS base stations to be generated for subsequent localization. The entire framework operates rapidly without requiring any prior data collection, and improves positioning stability and system availability using only standard ranging measurements and structural constraints at a low computational cost. To validate the proposed approach, multiple low-cost ultra-wideband (UWB) base stations were deployed in a corridor environment, and pedestrian motion data were collected under dense NLOS conditions. Experimental results demonstrate a substantial improvement in positioning performance: the root mean square error (RMSE) of UWB positioning is reduced from over 4 meters to below 0.4 meters, confirming the effectiveness of the proposed solution.

Fuzzy Cognitive Maps (FCMs) are a prominent soft computing technique for time series forecasting, valued for their ability to effectively model complex temporal dynamics. While FCM learning algorithms improve the performance of FCM-based predictors by capturing causal relationships between nodes, existing approaches predominantly rely on offline time series data stored in static repositories. This limitation hinders their adaptability to dynamic changes in map structures over time, making them unsuitable for real-time streaming data analysis and dynamic modeling of evolving causal relationships. Furthermore, the non-stationary nature of real-world time series presents significant challenges to the predictive performance of FCM-based models. To overcome these limitations, we propose a novel hybrid offline-online FCM learning algorithm that integrates a non-stationarity detection mechanism with a knowledge-guided least squares (KGLS) method. In the offline phase, an initial FCM-based predictor is constructed from historical data, where the recursive least squares (RLS) method is employed to capture long-term causal relationships using a sliding window technique. The online phase incrementally updates the model using streaming data, guided by a non-stationarity detection mechanism based on statistical hypothesis testing. The mechanism classifies data shifts into stable, warning, and drift levels. To mitigate catastrophic forgetting, the KGLS method maintains a compact yet representative memory buffer of past data samples. During training, these samples are replayed alongside new data, enabling the model to reinforce previously learned patterns while adapting to new information. Extensive experiments on stationary and non-stationary datasets demonstrate that our method achieves superior overall prediction performance and accurately forecasts trends in non-stationary time series in real time.

Interference has been a significant concern that may degrade the performance of autonomous train localization using Global Navigation Satellite System (GNSS), especially for safety-critical GNSS applications in railway transportation. Due to complexity and uncertainty of GNSS deception interference, GNSS-based train localization may significantly suffer from the vulnerabilities of an ordinary Train Position Unit (TPU), which would substantially hinder the trustworthy application of GNSS in railway systems. However, it is usually difficult to accurately characterize the effect of deceptive interference in misleading the position computation. Without relying on complex analytical theories, data-driven model-based detection has demonstrated great potential for practical applications. In this paper, we introduce a comprehensive initiative focused on developing an active detection strategy specifically designed to combat typical GNSS spoofing threats. The Conditional Tabular Generative Adversarial Network-TabTransformer (CTGAN-TabTransformer) solution is proposed for constructing the detection model, where CTGAN realizes the dataset augmentation and sample balance level enhancement. With this basis, a TabTransformer-based attack detection model can be derived effectively. Interference injection tests were carried out using real rail profile data. The results show the effectiveness of the proposed detection solution compared with different data-driven methods. Findings of this research provide an effective approach to enhance the resilience of GNSS-based train localization with a GNSS deception attack detection capability, which illustrates the potentials in enabling autonomous, safe and reliable state perception and operational control of modern railway trains.

In intelligent railway systems, spoofing attacks pose significant cybersecurity threats to train positioning based on Global Navigation Satellite System (GNSS). To enhance the resilience of GNSS Positioning, Navigation, and Timing (PNT) services against spoofing attacks, this paper proposes a spoofing mitigation solution based on Sparse Factor Graph Optimization (SFGO) for GNSS-based train positioning. In scenarios where some of the visible satellites are compromised by spoofing attacks, SFGO introduces the measurement biases caused by the attack as one of the states to be estimated within the global optimization framework. The estimated measurement biases are used to compensate for the spoofed measurements, enabling the restoration of accurate train state estimation. Results from spoofing attack injection tests provide compelling evidence that the proposed solution effectively mitigates the adverse impacts of spoofing on the positioning solution. It maintains a high positioning performance level even under challenging scenarios characterized by strong spoofing conditions. Furthermore, this solution operates at the position information processing level without modifying the GNSS receiver. It facilitates a seamless integration with the existing GNSS-based train-borne systems, offering a practical and efficient solution to ensure the resilience of GNSS-based train positioning against spoofing attacks.

GNSS (Global Navigation Satellite System) for train positioning has been applied in advanced train control systems. GNSS as the input for train localization, testing GNSS positioning for train localization as function and performance is a necessary procedure throughout the entire lifecycle of the train control system, from design to operation. To generate corresponding GNSS for train localization test cases, it is essential and beneficial to go through data mining of various train operation records and analyze the failure modes of the GNSS for train localization operations. This paper focuses on analyzing the GNSS data recorded during train operation, considering both textual records and numerical data generated by GNSS receivers. The textual records comes from regular records logged by the trainborne equipment of ITCS (Incremental Train Control System) collected on the Qinghai-Tibet railway line, while the numerical data is extracted from the processed NMEA (National Marine Electronics Association) data and RINEX (Receiver INdependent EXchange) format files. A semantic analysis method based on the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to mine the textual records, identifying important feature words and utilizing fault tree analysis to determine the causes of the failure modes. For the numerical data, a machine learning approach based on SSA-XGBoost (Sparrow Search Algorithm-eXtreme Gradient Boosting) is employed, followed by the application of the Shapley additive explanation method to identify the most important parameters. Through the mining and analysis of textual records, we identified that “Bad satellite geometry” “Poor satellite signal quality” and “Insufficient Number of visible satellites” are important fault causes when compiling test cases. FTA (Fault Tree Analysis) modelling method is used to determine the root cause of these failures as “positioning environment is restricted”. Through the mining and analysis of numerical data, we have determined that it is crucial to model the 3 key parameters of SNR_mean, HDOP, and PRerror_mean during the testing process. By mining and analyzing these textual records and numerical data, the paper provides clear directions and effective foundations for the generation of GNSS test cases for train localization. Based on the mining results, key failure modes and parameters to design more targeted and comprehensive test cases, improving testing effectiveness and comprehensiveness.

To address the challenge of stability analysis for traditional nonlinear fuzzy PID controllers in train virtual coupling, this paper proposes a dynamic modeling approach for speed errors based on the T-S fuzzy model. The T-S fuzzy model is constructed via fuzzy rules to achieve local linear approximation of nonlinear error dynamics. Leveraging Lyapunov stability theory, local asymptotic stability conditions are derived using Linear Matrix Inequalities (LMIs), and Particle Swarm Optimization (PSO) algorithm is employed for multi-objective optimization of PID parameters, considering response speed, control smoothness, and gain robustness comprehensively. Simulation results in virtual coupling following scenarios demonstrate that the controller achieves high-precision tracking, the mean error shows a 19.67% reduction compared to traditional fuzzy PID, the mean squared error exhibits a 20.51% reduction and control effort fluctuations are significantly reduced. This study establishes a local stability framework for T-S fuzzy control in virtual coupling systems, providing a theoretical basis for energy-efficient multi-objective optimization under complex dynamics.

The accumulation of inertial equipment errors in the GNSS/INS integrated system can degrade the accuracy of the train's position, velocity, and attitude (PVA) determination in the GNSS-difficult scenarios. To address this issue, a method for improving train attitude accuracy by employing inertial quaternion modelling is proposed. This method involves designing the quaternion estimation model assisted by the train acceleration in the inner-loop’s inertial derivation model (inner-loop estimation). When the INS operates in combined mode with the odometer, the inner-loop KF measurement model causes rotation errors due to the accelerometer’s bias. These errors can be transferred into the odometer, making it difficult to maintain the system's accuracy. To deal with this problem, the external-loop quaternion model is introduced. Utilizing error-quaternion as the coupling medium will ensure that the inner-loop’s quaternion and inertial-bias errors can be estimated and corrected in the external-loop, which involves integrating the inner-loop estimation model with the odometer/INS to generate the double-loop quaternion enhancement method. The experiment results on the Qinghai-Tibet Railway demonstrate that the proposed method has an obvious improvement in the PVA determination compared with odometer/INS method, and it can track the PVA references better and improve capability to maintain the PVA determination accuracy in GNSS-difficult scenarios.

The emergence of virtual coupling (VC) technology has the potential to substantially enhance the capacity of existing railway infrastructure. However, the complexity of line conditions introduces considerable disturbances to convoy operations, negatively affecting both energy consumption and operational efficiency. To address this issue, this study proposes a double loop trajectory optimization method for high-speed trains in a convoy, incorporating line condition disturbances and train dynamic characteristics. The proposed approach begins with an analysis of operating sequences under varying line conditions, leading to the development of a multi-resolution sequence optimization model for the leading train (LT). Subsequently, a train-following model is introduced to define the acceleration adjustment rules for the following train (FT) in different operating state. On this basis, a cooperative optimization framework is established to integrate the above models and define the detailed optimization procedure. Finally, a double loop seeker optimization algorithm is developed to obtain the optimal solution for the proposed model. Numerical experiments using field data from the Wuhan-Guangzhou high-speed railway line demonstrate the effectiveness of the proposed method. The experimental result proves that our method can generate trajectories with superior energy-saving and time-efficient performance while consistently maintaining safe and stable separation between virtually coupled trains.

This paper addresses the challenge of achieving high-precision headway estimation in virtual coupling trains by proposing a method utilising a high-order extended Kalman filter-based smoother. In this approach, the leading train uses a high-order extended Kalman filter to obtain its current operational state and then transmits historical state data to the following train. The following train then employs a high-order extended Kalman smoother to refine the state estimation and determine dynamic headway estimation. The high-order extended Kalman filter, based on Taylor series expansion, enhances state estimation accuracy by minimising truncation errors. It constructs a pseudo-linear representation of the full-space hidden variables and establishes high-order states, facilitating the modelling of measurements to align with the filtering derivation process. The high-order extended Kalman smoother continuously optimises current-state estimation using future measurement sequences, with the derivation process realised through the orthogonal theorem and innovation analysis. Ultimately, the headway estimation is updated based on the smoothed state provided by the smoother, and the effectiveness of the proposed method is validated through a multi-mode operating process of virtual coupling trains.

The integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) system has been widely used in vehicular positioning and navigation. However, the complex unstructured environments would lead to positioning degradation due to the GNSS outage. This paper proposed a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) assisted 21-dimensional GNSS/INS integrated navigation system using a Recomputed Method based on the Bias and Scale factor (BS-RM) error of the Inertial Measurement Unit (IMU). When GNSS is available, the obtained accurate GNSS/INS integrated navigation information is used as the input of the proposed BS-RM model to calculate the precise theoretical bias and scale factor error, which are trained as the target values of CNN-LSTM. When GNSS is unavailable, the trained CNN-LSTM is utilized to predict the accurate bias and scale factor. The consistent system positioning could be obtained with the suppressed INS error divergence by applying the IMU dead reckoning. To verify the performance of the proposed BS-RM model, three GNSS signal failure segments at different periods were randomly selected to evaluate the system. In addition, two GNSS failure segments were selected in the straight and curved roads respectively to further evaluate the performance of the CNN-LSTM assisted 21-dimensional GNSS/INS navigation system based on BS-RM. Compared with the traditional model of predicting position and velocity, the horizontal Distance Root Mean Square Error (DRMS) of BS-RM in the straight and curve tracks is improved by 83.85% and 88.06%, respectively, which confirms the improvement and consistent accuracy capability of the proposed method.

The on-board train control system is the core component in speed-interval control and safety assurance of the railway trains. The maintenance of the on-board train control system is of great significance to ensure the reliability and safe operation. However, the advanced condition-based maintenance mechanism has not been effectively applied, and the problem by imbalance sample data constrains the utilization of data-driven modeling methods to enable the advanced maintenance mode. To address these problems, this study proposes an enhanced fault modeling solution, which realizes the fault model using the XGBoost (eXtreme Gradient Boosting) method. Specifically, the DCGAN (Deep Convolutional Generative Adversarial Network) is adopted to achieve sample augmentation and enhance the capability of the XGFBoost-based fault model. Using practically collected fault log datasets from the on-board train control equipment, the performance of the proposed fault modeling solution with the sample augmentation capability is demonstrated, which reveals the potential in realizing the intelligent maintenance for practical operations.

The spoofing attack would be a serious threat to location-based applications based on Global Navigation Satellite System (GNSS). To mitigate the negative effect of the spoofing attack that makes the GNSS receiver obtain fake and misleading positioning information, the identification of the spoofing attack is a significant step before the countermeasure is adopted. In this paper, considering constraints of existing methods, SpoofSpike network, which is a novel post-correlation solution is proposed using the Spiking Neural Network (SNN). This GNSS spoofing identification scheme is based on the differences between the practically measured Cross Ambiguity Functions (CAFs) and the predicted one in the GNSS receiver information processing. Under the overall solution architecture, details about the spiking neuron model and the SpoofSpike network are given. The decision-making mechanism to identify the spoofing attack is analyzed. Results from the test and comparisons using the TEXBAT datasets illustrate that the SpoofSpike network-based solution is capable of realizing effective identification according to the comparison of the spoofing score with the threshold, and it outperforms other SNN-based models and the Artificial Neural Network (ANN) counterpart.

Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to preserve data privacy and reduce high data transfer costs, which has been applied in fault diagnosis. However, the imperfect data transmission problem due to communication errors easily results in low accuracy of FL-based fault diagnosis in the railway system. To solve the problem, a sequential and asynchronous federated learning framework is proposed for fault diagnosis of railway point machines (RPMs) in this work. First, a dual-branch network is proposed as the global model in asynchronous FL for reducing parameters, while maintaining high accuracy. Second, a time cycle mechanism based on sequential Kalman filtering is proposed for reducing the negative impact of data communication errors. Finally, experimental results demonstrates that the proposed method enhances the applicability of online RPM fault diagnosis training in real deployment scenarios.

Technological advances in the Global Navigation Satellite System (GNSS) industry have brought significant advantages in enhancing the cost-efficiency of railway applications. However, GNSS vulnerability to external jamming necessitates enhanced protection ability of the GNSS-based train localization system. This paper proposes a resilient train localization solution under the tightly-coupled integration scheme. This solution maximizes the utilization of multi-source information from train-borne sensors, including INS, odometer, and the trackmap database. Based on the existing localization scheme, it achieves a compatible way to address different jamming-intrusion situations without altering the GNSS receiver structure, addressing both the GNSS failure and degradation caused by jamming. Using an odometer/trackmap-enabled equivalent measurement logic, the continuity of localization can be guaranteed against GNSS failure under strong GNSS jamming. A robust filtering algorithm enabled by a jamming discrimination mechanism is proposed for GNSS/INS integration to mitigate the negative effect from degraded GNSS measurements, reducing the hazards by jamming with an intermediate power level. Based on the field data and a jamming test platform, results under two typical jamming scenarios are evaluated to demonstrate the necessity and superiority of the proposed solution. It also emphasizes the importance of the full-life-cycle resilience of GNSS-based train localization under the railway operation environment.

The Virtual coupling is proposed as a blocking mode to address the increasing demand for railway transport capacity, which takes operation efficiency further improve by separating trains with a relative braking distance. Nevertheless, an insufficient protection for complete avoidance safety risks in time with limited and fluctuate spacing separation between consecutive trains is introduced. A time-space occupancy band model is established to hold a safety protection in time-space dimension and assess the transport capacity for train operation under virtual coupling. In addition, a train trajectory planning method aimed at improvement of transport capacity, is proposed as a two-step program consisting of train followed operation trajectory planning based on Markov Decision Process and a trajectory multi-objective optimization for train convoy. In order to meet the requirement of the train trajectory dynamic adjustment under disturbance, an approach based on trajectory strategy set is designed by two stages to consider objectives of safety and punctuality. Based on the field data from the Wuhan-Guangzhou high-speed railway line, numerical experiments are conducted to validate the applicability of the proposed model and method. A comparative analysis of the track resource occupancy for several application condition under virtual coupling, and signaling systems is provided. The results indicate that the effective performance of proposed method in terms of trains separation and track resource occupancy, and show that virtual coupling is a more satisfactory blocking mode that could provide a higher track resource utilization while operation conditions are taken into account.

With the integration of 5G NR (New Radio) into railway systems, the demand for enhanced positioning and trajectory tracking performance in High-Speed Train (HST) networks has grown. However, many existing train positioning schemes rely on traditional algorithms like the Extended Kalman Filter (EKF), which may fall short of meeting the precision requirements, particularly in 5G HST scenarios. Addressing this limitation, this paper draws on the concepts presented by Ko et al. (2022) and introduces an Improved Extended Kalman Filter (IEKF) using the Least Squares of Undermeasurement (LSU) technique, specifically tailored for nonlinear systems. The IEKF, expanding step by step, theoretically captures statistical properties of the Knorr set for any order prediction error, providing richer information on higher-order terms compared to the traditional EKF. Additionally, for a more intuitive comparison of the IEKF unfolded to different orders, a novel performance indicator is introduced. In conclusion, to validate the effectiveness of our proposed algorithm in real-world scenarios, we demonstrate its superior localization performance by comparing Mean Squared Error (MSE) and Mean Absolute Error (MAE) with traditional nonlinear localization algorithms. The comparisons are based on simulation examples involving train localization tracking and an industrial device ablation system.

GNSS (Global Navigation Satellite System) is virtually becoming an autonomous train localization technology for the next-generation train control system. However, potential threats from the intentional interference may severely degrade the availability of GNSS due to its vulnerability. It is of great significance to detect and isolate the negative effects from GNSS interference for the Train Control System (TCS) in the railway field. For the protection against GNSS jamming, extra information from the Inertial Navigation System (INS) and odometer are involved, and an INS/odometer/trackmap-aided GNSS localization method for railway trains is raised in this paper. While the GNSS receiver cannot identify the real signals under a high-power jamming attack condition, a prediction deduced train position generation approach is proposed. In this strategy, velocity from the odometer and the geospatial constraint from the trackmap are involved to calibrate INS, with which continuous positioning is realized under a GNSS-denied situation. Furthermore, while the measurements degradation occurs caused by a relatively low power jamming, a residual-test-based detection solution based on the deviation between the predicted reference pseudo-ranges and the real ones is proposed to isolate degraded measurements. Results from an experiment under a GPS jamming condition demonstrate that the proposed solution outperforms the GPS Single Point Positioning (SPP) and the conventional GPS/INS method. The jamming protection and continuous positioning performance under specific jamming conditions enhance the capability of resilient train positioning.

Satellite-based train positioning has been an important focus for new generation railway train control systems. The Virtual Balise (VB) technology enables a compatible solution for introducing Global Navigation Satellite System (GNSS) into a train control system under the existing system specification framework. The VB capture operation requires accurate and reliable position information of the train, even under complicated and challenged operation environments. In this paper, the key logic of the Safety Qualifier Module (SQM) in the VB scheme is investigated. Using the integrity concept, specific detection and isolation logics are presented at both the GNSS observation level and the sensor fusion level. By actively adjusting the positioning calculation structure according to the quality inspection of the GNSS measurements, the qualification criteria can take advantage of the integrity report of the positioning result, which guarantees the dependability of GNSS-based VB capture for the train control purpose. Results from the filed data and simulation demonstrate the capability of the presented solution, which illustrates the advanced VB capture performance under specific GNSS observation scenarios.

Integrity monitoring is a crucial concern in Global Navigation Satellite System (GNSS) based positioning for railway transportation. The accurate detection and exclusion of the fault in GNSS measurements will greatly enhance the stability of the positioning performance for the safety critical application of GNSS. However, the existing Receiver Autonomous Integrity Monitoring (RAIM) method may fail in detecting and excluding the incipient fault due to the constraint of a Gaussian assumption. In this paper, a novel Fault Detection and Exclusion (FDE) approach with non Gaussian assumption is proposed to eliminate the effect from the incipient fault. Hatch filter is adopted to eliminate gross errors and smooth the observation but retain the incipient characteristic of deviated GNSS residual. A sliding window-based strategy is introduced to extract the empirical non Gaussian fault-free distribution using kernel density-based distribution fitting. Based on that, a global/local integrated test method is proposed to realize FDE, where the Kolmogorov-Smirnov test and Chi-square test are involved to detect the fault(s), and the improved Efficient Leave One Block Out (ELOBO) strategy is adopted to realize fault isolation. Results of fault injection tests under the GNSS-based train positioning scenario established with the field data demonstrate the performance of the proposal. In the comparison with the Gaussian-domain FDE method, the proposed approach realizes an enhanced sensitivity and exclusion capability to both the low level step fault and the incipient ramp fault. The fault exclusion capability ensures a stable positioning precision level, which is significant in GNSS-based train positioning for specific railway applications.

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.

Jamming to satellite navigation signals has become a major threat to the safety critical train control systems using Global Navigation Satellite System (GNSS), where the Virtual Balise (VB) concept enables a specification-compatible solution to utilize GNSS and reduce the physical Balises. This paper presents a novel GNSS jamming detection and exclusion solution to achieve trustworthy capture of VBs. An advanced architecture of Virtual Balise Reader (VBR) is proposed by integrating an Interference Qualifier Module (IQM) into the conventional GNSS-enabled VB framework. To enhance the trustworthiness of the VB capture, the IQM detects and identifies the existence of interference by examining the residuals between the real and predicted GNSS pseudo-ranges, which are generated by the odometry data and the trackmap. A discrimination test approach is utilized to evaluate the availability of raw satellite measurements according to the statistical analysis. We embed the performance indicator for each pseudo-range into the VB capture logic to isolate the degraded measurements and, meantime, adopt an adaptive Capture Interval Limit (CIL) to avoid the unexpected missed capture. Data sets from a GNSS jamming injection-based test platform are used for comparative studies, demonstrating the necessity and superiority of the proposed solution in improving the interference protection capability and trustworthiness of VB capture under the GNSS jamming environment.

Advantages of Global Navigation Satellite System (GNSS) in precise and reliable localization can be exploited to enable low-cost and train-centric railway train control systems by reducing track-side facilities like Balises and track circuits. However, constrained observability of satellite signals in specific railway station areas leads to a great challenge to the continuity and availability of GNSS-based train positioning. The pseudolite (PL) technology has a great potential for seamless localization under GNSS-challenged or even signal-denied environments. In this paper, we consider the optimized solution of pseudolite constellation design for seamless train positioning in railway station environments. An integrated train positioning architecture is presented based on a combined GNSS/PL measurement model. Using the trackmap, the proposed solution firstly establishes a feature point set covering all tracks in the station by extracting key Points-of-interest (POIs) from track database. Based on that, a K-means-enhanced generalized center-guided firefly algorithm (KGFA) is proposed to improve the standard firefly algorithm (FA) for deriving an optimized pseudolite constellation solution. The performance indicator for each candidate pseudolite layout scheme is defined according to the scenario-based GNSS/PL constellation configuration. The capability of the KGFA-enabled solution is validated by comparisons with similar FA methods. Through a case study, performance of the optimized pseudolite constellation and its influence to GNSS/PL-based seamless train positioning have been demonstrated over the involved reference pseudolite layout strategies. It is noteworthy that the proposed solution enables the enhanced inherent capability of the local pseudolite network to achieve seamless train positioning over the conventional GNSS-alone train positioning mode.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Train localization based on the Global Navigation Satellite System (GNSS) is an important feature of the novel train control systems. Considering the complicated railway operation conditions, jamming signals from the environment may pose a severe threat to the GNSS-based train localization. Therefore, the accurate detection and perception of GNSS jamming will play a significant role in ensuring the safe operation of the trains. In this paper, a jamming identification method for GNSS-based train localization using singular value decomposition (SVD) is proposed. By extracting feature values from the singular value sequence, and modeling the mapping relationship between the feature values and the jamming characteristics, the discrimination of jamming characteristics, including the type and the power of the jamming signal, is achieved. A satellite signal-level test platform with the jamming signal injection capability is built to verify the proposed solution. Results of the tests demonstrate the jamming recognition performance of the proposed solution under the Continuous Wave Interference (CWI), Linear Frequency Modulation (LFM) and the Band-limited White Noise (BLWN) jamming conditions.

Satellite-based positioning has become a significant technical feature of next-generation railway train control systems. However, the Global Navigation Satellite System (GNSS) enabled train positioning is susceptible to the threat from radio frequency interference, which may lead to risks to the safe and efficient train operation. It is of great necessity to evaluate the influence of GNSS jamming in developing specific anti-attack solutions in the railway applications. In this paper, tests of GNSS jamming scenarios are carried out through a jamming injection platform, with which the different signals that can be utilized in jamming are investigated, including (non-)coherent continuous wave, amplitude modulation, frequency modulation and bandwidth limited noise. The trackmap database is involved to evaluate the precision level of localization under jamming-injected conditions. The result analysis in terms of the cross-track error illustrates the degradation of the receiver under the threats from interferences, although there are different levels and characteristics among the involved jamming signals.

Global Navigation Satellite System (GNSS) performance varies in different environments, accuracy as the fundamental KPI depends on various factors, such as satellite orbit error, signal propagation delay in ionosphere and troposphere, and most importantly the effects on the ground as multipath. In railway train operation scenarios, the trains are travelling on the track passing through various environment scenarios, in several environment scenarios such as urban canyon, the GNSS signal can be easily blocked and reflected by the tall buildings and skyscrapers, which heavily reduces satellite visibility and satellite geometry. The multipath effect especially the non-line-of-sight (NLOS) signals will bring significant errors in user location solution. This paper proposes a GNSS NLOS signal propagation modeling method to evaluate the GNSS signal propagation path in the railway urban canyon environment near Shenyang North Railway Station. A 3D environment model is established, and a ray tracing technique is applied to model the signal propagation path, using the model and signal path, the results are used to evaluate and quantify the errors included in the NLOS signal. A field test is carried out as a performance comparison to the proposed method, the results showed that the simulation errors are consistent with the results in real environment field test, but the positioning results of field test is better than simulation results due to the receiver filtering algorithm inside the GNSS receivers.

Efficient and safe following operation of a high-speed train (HST) under the moving block system (MBS) has been the trend for high-speed railway (HSR) transportation. However, due to the complex operating environment and changeable operation state of HST during the following operation process, it becomes inapplicable for conventional train control approaches to satisfy HST's multi-objective operation demand including safety, punctuality, energy efficiency, and ride comfort. To resolve this problem, in this paper, practical models for the real-world characteristics of HSR's line parameters and HST's following control, as well as the multi-objective optimization model with some novel metrics, are established for achieving the optimal following control of HST under MBS. Besides, the multi-objective particle swarm optimization algorithm is modified with the preference information of control sensitivity and energy efficiency, so as to efficiently obtain the optimal following control strategy based on the real-time data of HST like velocity, location, and speed restriction. Furthermore, the convergence performance of our proposed method is demonstrated by comparative tests which also help select the proper algorithm parameters. Finally, the efficiency and feasibility of the proposed framework are illustrated by showing some experimental results.

Cooperative localization of the connected vehicles is significant for many advanced intelligent transportation system (ITS) applications. Vehicle-to-vehicle communication using dedicated short-range communication (DSRC) has great potential to enhance global navigation satellite systems (GNSSs) for the capability of cooperative localization. In the integration of DSRC and GNSS, the tolerance against the unknown and time-varying observation conditions is a key factor to fulfill the requirements of several specific ITS applications. Under a GNSS/DSRC integrated architecture for cooperative localization, a novel robust cubature Kalman filter (CKF) is proposed in this paper to improve the performance of the data fusion under uncertain sensor observation environments. In the proposed solution, the structure of the standard CKF is enhanced using the Huber M-estimation technique, in which the original measurement update in the CKF is modified considering the probable anomalies in state estimation. Furthermore, based on the investigation of the adjustment effect from the constraint factor, an adaptive strategy for this parameter is introduced to optimize the performance comprehensively. The proposed method is validated using a specific simulation system. Results of experiment and simulations demonstrate the capability of improving the robustness and adaptive performance over the original filters under the unknown operation conditions.

The accurate and reliable localization of the trains is one decisive factor for a lot of specific location-based railway applications. Considering the cost-efficiency of construction and maintenance, the Global Navigation Satellite System (GNSS) is an effective approach for train localization systems which aim to replace the track-side Balises with on-board sensors. Thus, the accumulative error of the odometer is calibrated by the GNSS receivers and the autonomy of the on-board equipment is surely improved. In order to cope with the uncertainties in raw sensor measurements, the Bayesian filtering frame is adopted to obtain an accurate estimation of the train's state. Based on that, an enhanced particle filter solution is presented to realize iterative estimation. In this method, the cubature Kalman filter (CKF) is involved to generate the proposal distribution by using the track constraint, which indicates a modified kinematical model and an extended measurement model. The coupling of track constraint is designed to generate the importance proposal distribution for the update stage of the sequential importance sampling. Results from simulation with field data demonstrate the capability of the track-constrained particle filter for train localization using GNSS and odometer, which is with great potential for enabling the next generation GNSS-based railway systems.

Trajectory planning plays a crucial role in train operation by providing with the authorized speed at each position. The traditional static train trajectory planning methods are always designed offline according to a preplanned timetable, and they ignored the uncertainties of parameters, resulted by line condition, resistance coefficient, and delay. These uncertain disturbances have not been considered adequately in previous studies. This paper deals with the dynamic optimal train trajectory planning problem with uncertainties. First, in order to identify uncertain resistance coefficients and calculate the dynamic limited speed, we present the optimization framework using onboard equipment such as a global navigation satellite system (GNSS) terminal, a power supply system, and a communication device to sample the real-time traffic information. Then, by taking the energy consumption and punctuality as objectives, we propose a moving horizon train trajectory planning optimization model with an adaptive weight allocation mechanism based on trip time error. The innovation of this paper lies not only in the establishment of a novel dynamic optimization model for train trajectory planning but also the strategy that combines real-time traffic information with the trajectory planning procedure. By contrast with most existing solutions, the proposed approach fully takes advantage of the real-time information and thus avoids the difficulties for modeling the uncertain coefficients for train trajectory planning. The efficiency of the proposed approach is illustrated by showing some numerical results of simulations with the infrastructure data from Beijing-Shanghai High-speed Railway of China.

A speed trajectory profile indicating the authorized train speed at each position can be used to guide the driver or the automatic train operation (ATO) system to operate the train more efficiently, which is the most important part of the Chinese Train Control System (CTCS) and will decide the safety and efficiency of train operation. The efforts produced by the train to follow the speed trajectory will directly affect the evaluation of train operation. This paper studies the optimization approach for the speed trajectory of high-speed train in a single section. First, we take the energy consumption as the measure of satisfaction of the railway company, and the trip time is being regarded as the passenger satisfaction criterion; then, we present optimal speed trajectory searching strategies under different track characteristics by dividing the section into some subsections according to different speed limitations. After that, we develop a multiobjective optimization model for the speed trajectory, which is subject to the constraints such as safety requirement, track profiles, passenger comfort, and the dynamic performance. For obtaining the Pareto frontier of train speed trajectory, which has equal satisfaction degree on all the objects, a hybrid evolutionary algorithm is designed and applied to solve the model based on the differential evolution and simulating annealing algorithms. By showing some numerical results of simulations, the efficiency of the proposed model and solution methodology is illustrated.

Satellite navigation system, especially the BeiDou Navigation Satellite System (BDS), has become a significant resource for many transport branches. It is strongly required that BDS is applied in modern railway transportation systems to support the rapid development of Chinese railway infrastructure and services. Currently, the BDS is still in the developing period, and the existing resources are not sufficient to support integrity assurance for many safety-related railway applications. The aim of this paper is therefore to develop a novel integrity monitoring method for the BDS-based train positioning with assistance from the additional dead reckoning system. In this method, the raw measurements of sensors are fused with the Bayesian filtering, and the self-weight adaptive particle swarm optimization with a combined objective function is involved to achieve an effective solution for the horizontal protection level which indicates the integrity capability. Field data are taken to validate effectiveness of the proposed solution and the advantages of the integrated particle fitness strategy. The implementation of this method will be positive for realizing fault detection and isolation for a series of safety-related railway applications based on BDS.

In the experiments and practical applications in a high-speed railway, it is observed that the carrier frequency of the sampled signal in a track circuit reader (TCR) is changed with train speed and goes beyond the upper permissive range prescribed for a jointless track circuit (JTC) in some cases. This can directly affect the availability of train target speed in train control systems and thus has an effect on the generation of the distance-to-go profile. It not only reduces the safety and efficiency of train traveling but also limits the improvement of train speed. To find the primary cause of the deviation in carrier frequency of the sampled signal in TCR (CFSST), this paper models the track-to-train continuous information transmission process using the transmission line theory based on the structures and principles of JTC and TCR. Then, the relation between the deviation in CFSST and the train speed is derived. Experimental results in high-speed railway have verified the correctness of the analysis, and the study can provides a strong theoretical basis for improving the safety level of railway traffic. Moreover, it can be a good reference for other countries where the similar track circuits are applied.

Train operation energy consumption occupies a large proportion of whole rail transport resource consumption. Aiming at improving energy utilization, current research is mainly about saving energy while security and time are limiting conditions. However, actual train operation is a complex process which has strict requirements on safety, energy consumption, precise parking and some other factors. This paper summarizes train control optimization research, analyzes train running characteristics and influential factors, and introduces train traction computing method. Multi-objective Particle Swarm Optimization with inertia weight algorithm is applied to study train operation optimization. With energy-saving, punctuality and precise parking being optimization goals, train operation optimization models and fitness evaluation functions are established. Through optimization, optimal operating condition sequence and corresponding condition conversion points are obtained. Effectiveness of the proposed algorithm is verified by offline simulations, and the results present good optimization performance.

Based on the exploration and development of Train Collision Early Warning System (TCEWS), the safety assurance overlay for high-speed train operation over the railway signaling system is becoming a reality in China. As the rapid development of BeiDou Navigation Satellite System (BDS), availability of precise and effective satellite navigation encourages a revolution in railway transportation system. It is becoming a general belief that Global Navigation Satellite System (GNSS) is recommended as the most autonomous, flexible and cost-efficient choice for location-based railway application. However, field experience of BDS-based positioning in train collision warning system tests suggests that several issues should be considered for application aspects. According to the performance requirements, this paper analyzes some significant points of the BDS-based train collision early warning system scheme. It is complemented by comparison and discussion with field test results of BDS and GPS enabled train collision warning implementations, which demonstrate the great potentials of BDS in this novel safety-related service.

Satellites are currently being used to track the positions of trains. Positioning systems using satellites can help reduce the cost of installing and maintaining trackside equipment. This paper develops a nonlinear combinatorial data reduction model for a large amount of railway Global Positioning System (GPS) data to decrease the memory space and, thus, speed up train positioning. Three algorithms are proposed by employing the concept of looking ahead, using the dichotomy idea, or adopting the breadth-first strategy after changing the problem into a shortest path problem to obtain an optimal solution. Two techniques are developed to substantially cut down the computing time for the optimal algorithm. The surveyed GPS data of the Qinghai–Tibet railway (QTR) are used to compare the performance of the algorithms. Results show that the algorithms can extract a few data points from the large amount of GPS data points, thus enabling a simpler representation of the train tracks. Furthermore, these proposed algorithms show a tradeoff between the solution quality and computation time of the algorithms.

In this paper the features of Qinghai-Tibet Railway and its requirement for a signaling system are introduced. To meet system requirements, a train control system which is based on satellite location and intelligent autonomous decentralized systems (IADS) technologies is discussed and planned to apply to low-density lines such as the Qinghai-Tibet line. The configuration of the system and some key technologies adopted in the system are also presented. Based on IADS, the system not only has autonomous controllability, flexibility of construction and on-line expandability. but also some intelligence.