Safety Margin Estimation Using Risk Assessment Method for Satellite-Based Train Localization Unit

The Chinese Railways and European Railways have similar features in terms of train operation and train control. The European Train Control System (ETCS) has been finalized as the technical standard train control system in Europe after more than ten years of effort. China has more than 71,500 of railway which includes conventional lines with a speed of 120 kilometers per hour, a dedicated passenger line with a speed of 250 kilometers per hour and the speed-raising line with a speed of 160 kilometers per hour. A high speed line running from Beijing to Shanghai with a speed of more than 300 kilometers per hour will be constructed in the next five years. At the present time there is technical standard for Chinese train control systems. In order to ensure the safety and efficiency of train operation and to meet the requirements of modern technical development in railways, the concept of CTCS (Chinese Train control System) was created in 2002 as the technical standard of Chinese train control systems. This paper introduces CTCS in both technical and management aspects and a comparison between ETCS and CTCS is presented.

This paper deals with the adoption of GNSS (Global Navigation Satellite System) applications for the evolution of ERTMS (European Railway Traffic Management System) system. We have studied a solution to monitor the train integrity and to accurately locate the position where the last coach of the train is stopped when a failure causes the split of the train into two sections. Train integrity is mandatory for the introduction of the “moving block” functionality foreseen by ETCS (European Train Control System) Level 3 that will bring major long-term benefits in terms of maintenance and operational capacity. The novelty of our approach lies in the exploitation of the multi-constellation capability to fast detect the train split with the shortest distance between the departed train sections compatible with the highest safety conditions set by the ETCS standard. The results are presented in a parametric form by varying the number of constellations and the probability of false detection and missed detection.

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring.

In sport science, Global Navigation Satellite Systems (GNSS) are frequently applied to capture athletes' position, velocity and acceleration. Application of GNSS includes a large range of different GNSS technologies and methods. To date no study has comprehensively compared the different GNSS methods applied. Therefore, the aim of the current study was to investigate the effect of differential and non-differential solutions, different satellite systems and different GNSS signal frequencies on position accuracy. Twelve alpine ski racers were equipped with high-end GNSS devices while performing runs on a giant slalom course. The skiers' GNSS antenna positions were calculated in three satellite signal obstruction conditions using five different GNSS methods. The GNSS antenna positions were compared to a video-based photogrammetric reference system over one turn and against the most valid GNSS method over the entire run. Furthermore, the time for acquisitioning differential GNSS solutions was assessed for four differential methods. The only GNSS method that consistently yielded sub-decimetre position accuracy in typical alpine skiing conditions was a differential method using American (GPS) and Russian (GLONASS) satellite systems and the satellite signal frequencies L1 and L2. Under conditions of minimal satellite signal obstruction, valid results were also achieved when either the satellite system GLONASS or the frequency L2 was dropped from the best configuration. All other methods failed to fulfill the accuracy requirements needed to detect relevant differences in the kinematics of alpine skiers, even in conditions favorable for GNSS measurements. The methods with good positioning accuracy had also the shortest times to compute differential solutions. This paper highlights the importance to choose appropriate methods to meet the accuracy requirements for sport applications.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

CORS station for synergistic monitoring of multivariate surface parameters in expansive soils

Autonomous train operation (defined by the on - board part of the ATO - Automatic Train Operation) is a key innovation in rail transport, bringing increased efficiency and safety. Ensuring ATO safety is a key challenge that can be addressed by integrat ion wi th the European Train Control System (ETCS). This paper focuses on using ETCS safety mechanisms to ensure the safety of autonomous train control. The following paper analyses the possibility of taking over the ETCS safety guarantees into the ATO . It focuse s on the different types of data provided by the ETCS, including static train data, dynamic data (movement authority, speed profile, gradient, localization, ETCS modes and traction) and information about the braking capabilities of the train. The ke y quest ion is whether this information can be relied upon to implement autonomous control without additional redundant mechanisms. According to the RAMS (Reliability, Availability, Maintainability, and Safety) analysis, existing safety features can be adop ted if the risks corresponding to the subsystem are sufficiently reduced and there is a secure link between the ATO and the ETCS systems . The specific physical interconnection via Ethernet plays an important role here. This link is described directly in the Europ ean Railway Agency specifications. Twelve packets of information are transmitted through this link. The aim is to verify whether it is po ssible to take over the safety guarantees of the ETCS for the ATO and to find the interface requirements between these systems. The main requirements of the ATO functions that process the ETCS information are to stop safely at a defined location, to keep to the schedule within the conditions specified by the ETCS, to optimize the speed for efficient traction energy consump tion and to start the station check - in sequence only after all safety conditions have been met. The methodology includes the analysis of the common interface between the ATO and the ETCS and applying RAMS procedures to identify and mitigate risks. The foll owing paper describes that the ETCS provides robust safety barriers during standard train operation at GoA 1 (Grade of Automation), allowing the ATO sys tem to rely on its safety features and focus on the additional risks associated with autonomous control at higher GoA levels. However, the key factor is to ensure a secure connection between the ATO and the ETCS in accordance with the applicable standards. Thi s paper should contribute to a better understanding of the links between the ATO and the ETCS. It al so provides important information for the further development of autonomous railway systems.

The GNSS NavAer project has been under development since 2017, focusing on creating a comprehensive infrastructure dedicated to monitoring and analyzing space weather phenomena. This project represents a significant investment in scientific research and technology, aimed at enhancing our understanding of various atmospheric and environmental processes. The infrastructure established through the GNSS NavAer project is versatile and can be deployed across several scientific disciplines, including Geodesy, Aeronomy, and Space Weather, among others.One of the primary motivations behind the GNSS NavAer project is the increasing reliance on Global Navigation Satellite Systems (GNSS) for navigation, communication, and positioning applications. As these systems become more integral to modern society, understanding the factors that can disrupt their performance is of great insterest. Space weather events, particularly those affecting the ionosphere, have been identified as significant sources of interference that can degrade the accuracy and reliability of GNSS signals.Ionospheric scintillation, a phenomenon characterized by rapid amplitude and phase fluctuations of GNSS signals, poses a particular challenge. This can result from natural irregularities in the ionosphere, often intensified by solar activity. When these irregularities are present, GNSS signals can experience disruptions that may lead to positioning errors, loss of signal lock, and degraded navigation accuracy. Such disruptions can have serious implications for air navigation, autonomous systems, and any application that relies on precise positioning data.In this presentation, we will focus on the specific use of the GNSS NavAer infrastructure for monitoring space weather, with an emphasis on IS. The infrastructure includes a network of strategically placed GNSS receivers that continuously collect real-time data on ionospheric conditions, which are stored in a databank and can be explored via a dedicated toll specially developed for that (ISMR Query Tool). By integrating advanced data processing techniques and algorithms, researchers can analyze these data to identify and characterize scintillation events as they occur.The significance of this monitoring cannot be overstated. By providing timely information on ionospheric conditions, stakeholders can better prepare for and mitigate the impacts of space weather on GNSS applications. For instance, real-time observations can help aviation authorities make informed decisions regarding flight operations, ensuring that aircraft can navigate safely even in conditions that would typically be challenging due to ionospheric disturbances.Furthermore, the GNSS NavAer project facilitates collaborative efforts among scientists and researchers in various fields. By sharing data and findings, the project promotes a more comprehensive understanding of space weather and its impacts. International cooperation is vital, as space weather events often affect multiple regions simultaneously, making a collaborative approach essential for effective monitoring and response strategies.In conclusion, the GNSS NavAer project is a groundbreaking initiative aimed at enhancing our ability to monitor and understand the complexities of space weather. Through its dedicated infrastructure, it addresses the challenges posed by IS and aims to provide solutions that improve the reliability of GNSS applications. The deterioration in positioning and navigation will be presented, together to some trial to reduce such problem.

People have to move between indoor and outdoor frequently in city scenarios. The global navigation satellite system signal cannot provide reliable indoor positioning services. To solve the problem, this article proposes a seamless positioning system based on an inverse global navigation satellite system signal, which can extend the global navigation satellite system service into the indoor scenario. In this method, a signal source is arranged at a key position in the room, and the inverse global navigation satellite system signal is transmitted to the global navigation satellite system receiver to obtain a preset positioning result. The indoor positioning service is continued with the inertial navigation system after leaving the key position. The inverse global navigation satellite system seamless positioning system proposed in this article can unify indoor and outdoor positioning using the same receiver. The receiver does not need to re-receive navigation information when the scene changes, which avoids the switching process. Through the design of signal layer coverage, the receiver is in a warm start state, and the users can quickly fix the position when the scenario changes, realizing quick access in a true sense. This enables the ordinary commercial global navigation satellite system receiver to obtain indoor positioning capability without modification, and the algorithm can perform accurate positioning indoors and outdoors without switching.

In this paper, trajectory optimisation algorithms developed specifically for the Global Navigation satellite System (GNSS) Avionics-Based Integrity Augmentation (ABIA) system are presented. The ABIA system is designed to increase the levels of integrity and accuracy (as well as continuity in multi-sensor data fusion architectures) of GNSS in a variety of mission- and safety-critical aviation applications. The trajectory optimisation algorithms can be employed both for flight planning as well as real-time optimisation of manned and unmanned aircraft for all flight phases. Three and six Degrees-of-Freedom (3-DoF/6-DoF) aircraft dynamics models are adopted to generate a number of feasible flight trajectories that also satisfy the GNSS constraints. A detailed simulation case study is presented to evaluate the performance of trajectory optimisation algorithms for GNSS integrity augmentation using an AIRBUS A320 3-DoF aircraft dynamics model. Results confirm that the employed trajectory optimisation algorithms are capable of supporting high-integrity tasks when GNSS is used as the primary source of navigation data.

Compaction quality control in filling construction is of great significance to the stability and durability of the face rockfill dam. The conventional method of quality control mainly relies on manual process control and inspection for a limited number of test holes, which cannot meet the high requirements of modern mechanized construction and schedule anymore, with increasing of scale of face rockfill dams. There is an urgent need to propose a new quality control method of face rockfill dams during the entire compaction process. In this paper, a supervisory system based on GNSS (Global Navigation Satellite System) technology, wireless data communication technology, Internet of things technology, and computer technology is developed to supervise the real-time roller compaction parameters of the working surface including rolling track, rolling times, rolling speed, thickness, and smoothness. The system obtains continuous and high-precision spatial position information of roller compaction machines through GNSS technology and then calculates the roller compaction parameter information. The compaction quality control for the face rockfill dam is achieved through the supervision of roller compaction parameters. The feasibility and robustness of the developed supervisory system are validated by a case study in the face rockfill dam of Shuibuya project in China. The practice shows that the system provides a new and effective method of process control for the construction quality of the roller compaction in dam engineering and realizes real-time, precision, and automatic supervising of roller compaction parameters and ensures better construction quality.

The Choushui River alluvial fan is an important agricultural production region in western Taiwan. This insufficient level of rainfall replenishment, combined with the unauthorized use of wells to irrigate crops, exacerbated land subsidence in the region. In this study, levelling, global navigation satellite system (GNSS), and interferometric synthetic aperture radar (InSAR) observation data were collected from 2018 to 2021 for analysis. The GNSS data were processed using the precise point positioning method, and the results were compared with data processed using the traditional static relative positioning method. The 3-year cumulative subsidence at the subsidence centre was approximately 9 cm in Changhua and 19 cm in Yunlin. To compensate for the lack of spatial resolution of GNSS and improve the accuracy of InSAR, the InSAR, GNSS, and levelling data were integrated, and these results were validated against levelling check points. They showed an average error of 0.4 cm/year in the annual subsidence rate. Overall, the GNSS had high precision and continuity, making it suitable for real-time early warning of land subsidence. InSAR obtained data with higher spatial resolution, allowing for early detection of new land subsidence areas and comprehensive monitoring of changes in land subsidence across the entire region.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

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 results. The 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 features. The 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.

In the current era, which is characterized by increasing demand for high-precision location and navigation capabilities, various industries, including those involved in intelligent vehicle systems, logistics, augmented reality, and more, heavily rely on accurate location information to optimize processes and deliver personalized experiences. In this context, the integration of Global Navigation Satellite System (GNSS) and inertial sensor technologies in smartphones has emerged as a critical solution to meet these demands. This research paper presents an algorithm that combines a GNSS with a modified downdate algorithm (MDDA) for satellite selection and integrates inertial navigation systems (INS) in both loosely and tightly coupled configurations. The primary objective was to harness the inherent strengths of these onboard sensors for navigation in challenging environments. These algorithms were meticulously designed to enhance performance and address the limitations encountered in harsh terrain. To evaluate the effectiveness of these proposed systems, vehicular experiments were conducted under diverse GNSS observation conditions. The experimental results clearly illustrate the considerable improvements achieved by the recommended tightly coupled (TC) algorithm when integrated with MDDA, in contrast to the loosely coupled (LC) algorithm. Specifically, the TC algorithm demonstrated a remarkable reduction of over 90% in 2D position root mean square error (RMSE) and a 75% reduction in 3D position RMSE when compared to solutions utilizing the weighting matrix provided by Google with all visible satellites. These findings underscore the substantial advancements in precision resulting from the integration of GNSS and INS technologies, thereby unlocking the full potential of transformative applications in the realm of intelligent vehicle navigation.