Search

Applications of global navigation satellite system (GNSS) in land transportation systems are already extensively deployed and will certainly continue to grow especially in the framework of intelligent transport systems. However, one of the best-known drawbacks of such a system is the lack of satellite visibility in dense urban areas as well as in some specific embedded railway environments. This restricts considerably GNSS use for extended safety related applications. In this paper, a new tool is proposed to predict the availability of a satellite constellation from the point of view of the land transportation user. Knowing the trajectory of a land vehicle, the tool predicts the number of satellites that will be received and produces a safety criterion able to qualify the GNSS localization result. A first version of the tool, already in operation, merges an image processing approach providing the knowledge of the land environment, and the output of a satellite tracking program predicting satellite positions in the sky. This allows us to determine, using a simple optical approach, the number of satellites received in line-of-sight or blocked, with regard to the nearby environment of the receiving antenna. Results obtained in railway as well as in road environments show that satellite signals received by multipath are often used by GNSS receivers in the localization process. Thus, propagation characteristics of the satellite signals in an urban canyon configuration were characterized to determine when a signal received by reflected ray is used by the receiver or not. A criterion related to the satellite elevation is defined to improve the overall performance of the predictive tool. Comparisons with real measurements are commented on. Both simulations and measurements are very similar. Index Terms--Availability, localization, multipath, radio electric coverage, satellites.

This article provides an overview of the use of inertial and visual sensors and discusses their prospects in the Arctic navigation of autonomous vehicles. We also examine the fusion algorithms used thus far for integrating vehicle localization measurements as well as the map-matching (MM) algorithms relating position coordinates with road infrastructure. Our review reveals that conventional fusion and MM methods are not enough for navigation in challenging environments, like urban areas and Arctic environments. We also offer new results from testing inertial and optical sensors in vehicle positioning in snowy conditions. We find that the fusion of Global Navigation Satellite System (GNSS) and inertial navigation systems (INSs) does not provide the accuracy required for automated driving, and the use of optical sensors is challenged by snow covering the road markings. Although extensive further research is needed to solve these problems, the fusion of GNSS, INSs, and optical sensors seems to be the best option due to their complementary nature. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) integrity is defined as a measure of trust that can be placed in the correctness of the information supplied by the total system. Initially developed for safetycritical applications in the aeronautic domain, this concept has attracted more and more attention from terrestrial GNSS-based applications in recent years. The main problem of integrity monitoring for urban transport applications is related to GNSS signal degradation due to local effects such as multipath and non-line-of-sight reception. This article proposes two integrity monitoring schemes with two classes of approaches, based on either the snapshot weighted least-squares residual or sequential weighted extended Kalman filter innovation. The integrity monitoring schemes are mainly realized by two modules: accuracy enhancement, in which measurement errors are better characterized, and integrity monitoring, in which one of the fault detection and exclusion techniques, i.e., the Danish reweighting method, is applied. The results with real GPS data collected in urban canyons show that the proposed system can effectively improve positioning accuracy and guarantee system integrity. [ABSTRACT FROM AUTHOR]

According to the evolution tendency of the control decision process from a trackside to a train-borne system, various autonomous localization units for railway vehicles were developed. As recommended in railway standards, the design process of each system, here the autonomous localization units (LU), follows the V-model whose first step is to define its availability requirement in order to satisfy the global ETCS system requirements. The classical approach for assigning the subsystem availability is based on the assumption that failure parameters of other units are precisely known. This assumption is too restricted in reality due to the lack of information. In this paper, we propose a new approach that allows taking into account uncertainties in the dependability parameters of the ETCS components for identifying the upper threshold of the LU unavailability to reach ETCS availability requirements. Using fuzzy fault trees, the fuzzy unavailability of the ETCS without the autonomous LU is evaluated. Then, based on its membership function, we assess the satisfaction rate that an advanced ETCS with the autonomous LU can satisfy the ETCS availability target. [ABSTRACT FROM AUTHOR]

Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from urban GNSS users. However, the algorithms developed for the aerospace domain cannot be introduced directly to the GNSS land applications. This is because a high data redundancy exists in the aviation domain and the hypothesis that only one failure occurs at a time is made, which is not the case for the urban users. The main objective of this paper is to provide an overview of the past and current literature discussing the GNSS integrity for urban transport applications so as to point out possible challenges faced by GNSS receivers in such scenario. Key differences between integrity monitoring scheme in aviation domain and urban transport field are addressed. And this paper also points out several open research issues in this field. [ABSTRACT FROM AUTHOR]

Railways have already introduced satellite-based localization systems for non-safety related applications. Driven by economic reasons, the use of these systems for new services and, in particular, their introduction in signaling system is seriously investigated today and tested all around the world. Because of the weight of their history, their strong normative context, and the high requested level of safety, the introduction is relatively slow. The aim of this paper is to provide a survey of past and current programs dealing with global navigation satellite systems as a basis to introduce main issues relative to context, standards, performance requirements, and safety proofs. Links with aeronautical concepts are also presented, illustrating the transposable principles and the limits due to the land transport environment. [ABSTRACT FROM PUBLISHER]

The article presents information on several meeting on telecommunication and additive manufacturing which sponsored by Institute of Electrical and Electronics Engineers (IEEE) Communications Society that was held in 2017. Topics include security for 5G wireless communication system, autonomous vehicles, and optical networks.

In global positioning systems (GPS), classical localization algorithms assume, when the signal is received from the satellite in line-of-sight (LOS) environment, that the pseudorange error distribution is Gaussian. Such assumption is in some way very restrictive since a random error in the pseudorange measure with an unknown distribution form is always induced in constrained environments especially in urban canyons due to multipath/masking effects. In order to ensure high accuracy positioning, a good estimation of the observation error in these cases is required. To address this, an attractive flexible Bayesian nonparametric noise model based on Dirichlet process mixtures (DPM) is introduced. Since the considered positioning problem involves elements of non-Gaussianity and nonlinearity and besides, it should be processed on-line, the suitability of the proposed modeling scheme in a joint state/parameter estimation problem is handled by an efficient Rao-Blackwellized particle filter (RBPF). Our approach is illustrated on a data analysis task dealing with joint estimation of vehicles positions and pseudorange errors in a global navigation satellite system (GNSS)-based localization context where the GPS information may be inaccurate because of hard reception conditions. [ABSTRACT FROM PUBLISHER]

As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible solution to the issue. For prior-map-free situations, as the only sensor with a global frame, RTK determines and maintains the global positioning precision of the integrated system. However, RTK performance degrades greatly in GNSS-challenged environments, and most of the existing integrated systems adopt loose coupling mode, which does nothing to improve RTK and, thus, prevents integrated systems from further improvement. Aiming at improving RTK performance in the RTK/LiDAR/INS integrated system, we proposed an innovative integrated algorithm that utilizes RTK to register LiDAR features while integrating the pre-registered LiDAR features to RTK and adopts parallel filters in the ambiguity-position-joint domain to weaken the effects of low satellite availability, cycle slips, and multipath. By doing so, we can improve the RTK fix rate and stability in GNSS-challenged environments. The results of the theoretical analyses, simulation experiments, and a road test proved that the proposed method improved RTK performance in GNSS-challenged environments and, thus, guaranteed the global positioning precision of the whole system. [ABSTRACT FROM AUTHOR]

The robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land- and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configurations. This work first provides new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focuses on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based on only the single frequency. The detection by means of logistic regression not only provides a binary LOS/NLOS decision but also an associated probability which can be used in the future as a means to weight-specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state-of-the-art algorithms, reaching 90% detection accuracy in the validation scenarios evaluated., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 García Crespillo, Ruiz-Sicilia, Kliman and Marais.)