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ION GNSS + 2022, Denver, ETATS-UNIS, 21-/09/2022 - 23/09/2022; In safety-critical applications, the vulnerability of the Global Navigation Satellite System (GNSS) to radio frequency interferences (RFIs) is a major concern. The development of a fail-safe and robust localization system now necessarily requires detection and mitigation of such interferences, which has increased with the proliferation of wireless systems and easy access to personal privacy devices (PPD) also known as jammers. Moreover, the advent of software-defined radio (SDR) permitting the transmission of customized and more complex signals has further complicated this problem. This paper provides an in-depth comparison of interference mitigation techniques at multiple levels primarily focusing on the safety perspective at the position level. For this purpose, the classical adaptive notch filter (ANF) and the wavelet packet decomposition method are applied to mitigate the effects of interference. This comparative study aims to assess the performance of these mitigation techniques at multiple levels (frequency estimation, acquisition, tracking and position). However, the main goal for the final user is to study the impact on key performance indicators (KPIs) such as accuracy, availability, and safety. Furthermore, two cases are considered: 1): interference in the absence of any mitigation strategy and after applying interference mitigation technique at the pre-correlation level. The complete study is presented for two different types of time-varying continuous wave (CW) interferenc.e signals namely frequency hopping and the chirp signal.

CTS 2021, 16th IFAC Symposium on Control in Transportation Systems, Lille, FRANCE, 08-/06/2021 - 10/06/2021; European Train Control System (ETCS) is the signalling and control component of the European Rail Traffic Management System (ERTMS). This system is essential to guarantee the safe and interoperable operation of trains. To enhance the competitiveness of rail transport services, the introduction of innovative solutions are under study in view of the evolution of ETCS. In this context, the adoption of Global Navigation Satellite System (GNSS) for train localization is investigated as a technology which can ensure an undeniable added value for railways. Yet, a main challenge is to provide safety evidence permitting the certification of these new systems. In particular, the classical safety analysis approaches show limitations in dealing with the complexity of such systems. Therefore, more adapted safety and performance analysis techniques have to be elaborated. In this paper, a model-based approach, adapted for the evaluation of GNSS-based localisation systems in railway, is presented. Considering the safety-critical aspect of the localisation function in railways, formal methods which are based on rigorous mathematical foundations are adopted in the present work. Concretely, a set of formal models are elaborated to ensure a modular representation of trains dynamics in the context of GNSS-based localization. Namely, probabilistic timed automata formalisms are adopted to this aim. Such notations allow for considering stochastic and dynamic aspects, so as to reflect reality in a trustworthy way. The safety and performance properties to be checked can then be formulated by means of temporal logics. Finally, the analysis of such features can be achieved by means of model-checking and simulation techniques. This evaluation phase yields both qualitative and quantitative results and allows for assessing the impact of various parameters and functional choices on both safety and performance. UPPAAL-SMC engine was used to set the tooling chain of our approach, and an illustration considering specific operational test cases is provided.

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 safety critical 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.

ION ITM 2022 - International Technical Meeting of Institute of Navigation, Long Beach, CA, ETATS-UNIS, 25-/01/2022 - 27/01/2022; Localization function for an advanced intelligent transport system, such as an autonomous vehicle, must ensure various operational requirements such as safety, accuracy, availability and continuity of service, anytime, anywhere, at a reasonable cost. Global Navigation Satellite System (GNSS) have many advantages insofar as they present the most accessible technology to the user to determine its position with a certain accuracy without prior knowledge. However, in an environment where signal reception may not be optimal especially due to phenomena such as satellite blockage, multipath, intentional or unintentional interferences and spoofing, it becomes very challenging to meet all these requirements, especially those related to operational safety. The latter is measured by evaluating the integrity of the localization function. It can be evaluated through a Protection Level, which is calculated by the receiver to self-monitor its integrity, also called RAIM (Receiver Autonomous Integrity Monitoring). In the literature, integrity in presence of multipath and NLOS has been extensively investigated [1] as well interference detection and mitigation solutions [2]. However, the impact of interference presence and mitigation on integrity monitoring is not deeply addressed yet. In this study, we evaluate some key performance indicators (KPI's) for GNSS users. These indicators will be evaluated for three different cases; 1) when no interference is applied and clean GNSS signals are processed. 2) In the presence of interference but without any mitigation technique. 3) After applying a mitigation technique at the pre-correlation level to filter the interference signal. The mitigation technique relies on state-of-the-art Notch filters provided by a Septentrio receiver. The interference signals are generated in the laboratory to produce disturbances in the GNSS band. Thus, and thanks to the a priori knowledge of the true position, it is possible to establish the Stanford diagrams for these cases. A deep analysis of performance in the presence and absence of interferences and in the presence and absence of a mitigation technique allows the first conclusions to be drawn on the evolution of accuracy, availability and operational safety indicators. The preliminary results reveal the importance of considering, from the design phase of the localization function, the possibility of dealing with this phenomenon, in particular in the measurement weighting model to use for enhanced performance.

From Inertial Signals to Precise Pedestrian Indoor Positioning:Assessing Human Gait

GNSS are widely used in localization applications. These systems have the advantage of being able to locate, at low-cost, receivers anywhere on the planet's surface without prior knowledge of the position. However, the accuracy of the provided position strongly depends on the quality of reception and on the number of signals received. And these depend on the environment around the receiver. Indeed, certain environments such as urban canyons or forests are known to be challenging because multiple phenomena such as multipath, Non-Light Of Sight (NLOS), or interference are common there. These phenomena induce an erroneous estimate of the position. In applications where the localization function does not constitute a critical function involving the safety of the overall system, this erroneous estimate only constitutes an inconvenience. But in applications for which the safety of goods or people is at stake, the tolerance of what is called the integrity risk is infinitesimal, not to say null. Moreover, in this type of application, it is not so much the position that matters but the ability to limit the unknown position error. For that, the literature proposes different possibilities of protection level calculation. The ideal protection level would be the one that limits the unknown error as closely as possible without ever underestimating it. This makes it possible to minimize hazardous operations as well as unavailability of the localization function. In this paper, we are interested in the impact of GNSS observation weighting models on the protection levels. We propose a model capable of carrying a multi-criteria and multi-parametric strategy allowing a better adaptivity to the navigation context. The encouraging experimental results show that a parametric and constrained modelling strategy of errors, from the design step, relaxes the requirements on the calculation of the protection levels. They also show the complementarity between the characterization of errors which must be concerned with the functional behaviour and a Fault Detection and Exclusion layer which is responsible for the dysfunctional behaviour.

ICE Seminar, PARIS, FRANCE, 04-/02/2021 - 04/02/2021; Over the past 15 years, there has been an exponential growth of new technologies for indoor positioning and navigation. Unlike GNSS technology, which has become the leading solution for outdoor positioning, no technology has taken the lead indoors. With the price drop of radio beacons, we see massive deployment of beacons' networks for positioning. Image processing is progressing fast thanks to machine learning techniques that improve the rendering of very low-cost cameras. More and more smart devices embed inertial sensors providing autonomous navigation options. These are only a few of the technologies deployed. Hybridizing technologies to find the best compromise between accuracy, cost and energy consumption is at the heart of ongoing development. The nature of sensors in the infrastructure or smart devices, specific use cases requirements and privacy concerns about geolocated data are all features used to choose the right technologies to hybridize. Adopting a ubiquitous approach that combines dead reckoning and absolute positioning while recognizing the application and environmental context is certainly a strong trend in current developments and research. Given the great diversity of existing positioning systems and ways of presenting their performance, it seems almost impossible to provide a clear comparison of localization performance. The key to this comparison certainly lies in experimental comparative trials, in the same context and on identical scenarios. This approach started several years ago with international positioning competitions. This talk will review main indoor positioning technologies according to several comparison criteria. It will also exploit the results of the 2018 Indoor Positioning Indoor Navigation(IPIN) international competition that took place in a 9'000m² shopping mall (Atlantis) in Nantes (France) where 49 teams competed.

ION ITM 2021 - International Technical Meeting of Institute of Navigation, Virtual, , 25-/01/2021 - 28/01/2021; The accuracy of the satellite-based positioning system is severely affected in the presence of local phenomenons like multipath, NLOS and interferences. These local effects are difficult to model due to spatial and (or) temporal dependencies. This study aims to model the position error on the track and later use it for the track classification. The characterization of the track errors can be beneficial as it can help to increase the capacity of the railway track by providing adaptable protection level and also for sending warning alerts to the other trains if any of them is in degraded mode to avoid any sort of accidents. We present a very simple machine learning approach to model the position error. For this purpose, we investigate the Expectation-Maximization (EM) algorithm to estimate the parameters of the Gaussian Mixture Model (GMM). We intuitively selected 3 classes to represent different error shape features to avoid complexity and to keep some diversity. In the learning phase, 5 different runs are used to capture as much as the error variations on the track throughout the day. The models estimated in the learning process is applied to the test samples for the track characterization. The results show that class representation varies due to the error variation within the day. For some particular obstacles, the track is always represented by the class that shows deteriorated conditions. It also shows that the class representation is very similar to the learning samples when the error models are applied to the test sample collected on a different day with a nearly similar satellite configuration.

Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. In this context, this paper conducts a review of popular SLAM approaches with a focus on vSLAM/viSLAM, both at fundamental and experimental levels. It starts with a structured overview of existing vSLAM and viSLAM designs and continues with a new classification of a dozen main state-of-the-art methods. A chronological survey of viSLAM’s development highlights the historical milestones and presents more recent methods into a classification. Finally, the performance of vSLAM is experimentally assessed for the use case of pedestrian pose estimation with a handheld device in urban environments. The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.

The use of bicycles is regaining popularity, especially in city centers where they can be used as quickly as cars and reduce carbon footprint. However, in these dense urban environments, navigation methods based on GNSS technologies do not provide sufficient accuracy for cyclist navigation and safety. To mitigate the challenges of indoor-like surroundings, a new positioning algorithm: BIKES (Bicycle Itinerancy Kalman filter with Embedded Sensors) was developed. This extended Kalman filter processes deeply degraded GNSS data to update velocity and position estimates with differential computation approaches working even in degraded environments. GNSS signals are combined with inertial and magnetic data to continuously estimate the trajectory when GNSS is unavailable. BIKES' performance was tested in real-life conditions on a 3 km long path in the city center downtown Nantes and compared to Google Fused Location Provider estimates. A mean positioning error below 1 m with a 0.5 m standard deviation is achieved. These results are 4 times better than the Google solution. This algorithm allows also distinguishing if the cyclist is riding on a bike path, the sidewalk, or the road, which is critical for guidance systems.

L'article présente le projet ERSAT GGC et les techniques de détection des effets locaux sur les GNSS développées pour l'introduction du GNSS dans le système ERTMS/ETCS

In France, the railway lines are divided into the national and international lines. While the former has a proper implementation of the railway signalling systems as relay-based or computer-based systems, the latter follows the guidelines defined by the ERTMS standards, focusing on the interoperability throughout Europe. In a previous work we presented how the national legacy relay-based Railway Interlocking Systems (RIS) can evolve to use a computer-based technology in order to benefit from its advantages. In this present article, we present how these computer-based systems can be used in order to allow the evolution from a national line towards an ERTMS compliant international line. This proposition is based on the fact that the previous work provides the support for evolving the track-side technologies with, for instance, the use of GNSS for the train geo-localisation instead of electrical circuits-based train detection systems. Then, a discussion of the impact of this evolution over the system safety is presented.

Lambda Mu 22, 22e Congrès de maîtrise des risques et de sûreté de fonctionnement. Les risques au coeur des transitions (e-congrès), Virtuel, FRANCE, 24-/11/2020 - 24/11/2020; A modular and parametric approach to evaluate the safety of GNSS-basedsystems in railway signallingis presented in this paper.The complex system is broken down into modules that areformally modelled. This work is part ofa generic safety method.; Ce papier présente une approche modulaire et paramétrique pour évaluer la sécurité des systèmes avec GNSS dans la signalisation ferroviaire. Le système complexe est décomposé en modules puis modélisé de façon formelle. Ce travail s'inscrit dans le cadre d'une méthode de sécurité générique.

ENC 2020, 28th European Navigation Conference, Dresden , ALLEMAGNE, 23-/11/2020 - 24/11/2020; The work shows the high level concept and methodology followed for designing a geo-distributed simulation and verification infrastructure connecting remotely GNSS excellence centres and ERTMS/ETCS laboratories to evaluate the GNSS performances in the railway environment. Proper methodology and tools are adopted to simulate GNSS behaviour in different railway scenarios in nominal or in presence of global and local hazards. Particularly, the test-bed main goals are: i) achieving a realistic characterization of the environment in terms of railway and GNSS infrastructures able to evaluate the performances and properties of some fail-safe train positioning components in nominal and fault conditions; ii) defining a common test process framework for zero on-site testing instead of testing on-site saving effort and time. The test-bed offers the unique advantage to stress the global system in presence of very rare GNSS fault events instead of performing long and expensive measurement campaigns on field for detecting them and analysing their impact on ETCS. The work will show its description and the methodology adopted for its design and implementation.

ENC 2020, 28th European Navigation Conference, Dresden , ALLEMAGNE, 22-/11/2020 - 25/11/2020; GNSS observations weighting models have received attention in the past decades. Several reasons can explain this gain in consideration. It mainly corresponds to the emergence of autonomous, global, continuous, precise and integrity-compliant localization systems. Indeed, for safety critical applications, adopting a naive strategy of observations' equal weighting could not guarantee to achieve all goals, and even more for terrestrial applications where the local environment can strongly disturb signals reception. The existing observation models use one or two metrics to estimate the confidence to be given to an observation. The first is the satellites' elevations. This model considers that the satellites with low elevation are more subject to global errors (orbit and atmosphere errors) or local multipaths or NLOS phenomena. The second criterion is the carrier to noise ratio provided by the receiver. However, combining these two criteria by a simple multiplication of the two models taken separately, presents a limit: the result is no more « under control ». In this study, we propose to formulate a new weighting strategy using these two criteria more guided by expertise's expected results.

ENC 2020, 28th European Navigation Conference, Dresden , ALLEMAGNE, 22-/11/2020 - 25/11/2020; For land transport environment and railways in particular, the main limits of GNSS signal exploitations for an accurate and safe positioning comes from the local environment surrounding the vehicle. Obstacles induce multipath and non-line of sight signal reception that need to be detected for railway line characterization or feared event mitigation. In this paper, we show that the use of two techniques based on CN0 and camera respectively, can help to detect and label faulty events along a trajectory. The methods have been applied on a test run performed during the ERSAT GGC project in Spain. This paper presents first the techniques, then an analysis of the detections for evaluation.

ESREL 2020 PSAM 15, 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, Venise, ITALIE, 01-/11/2020 - 06/11/2020; The introduction of satellite-based localization techniques, relying on GNSS (Global Navigation Satellite Systems), for railway operation is a promising, though challenging issue. Such a technical breakthrough aims to achieve a substantial gain for the railway sector in terms of competitiveness. In recent years, several projects have investigated the introduction of GNSS in railways. However, no proposed solution has been proven to fulfill the railway safety requirements. Moreover, traditional safety assessment methods present limitations in dealing with the complexity of such systems, raising the need to set up new approaches to this aim. The work discussed in this paper is part of a general model-driven approach for safety evaluation of GNSS-based localization in railway operation. In particular, we discuss the main features of our model-based approach and we elaborate on the different safety features that can be investigated based on our models. Considering the safety-critical aspect of the localization function, formal verification methods based on model-checking are adopted to provide safety evidence. Concretely, the overall behavior of the system will be expressed in a modular representation based on the timed automata notation supported by the UPPAAL tool. Such a modular approach permits to consider a variety of system architectures in different operational contexts. We also provide an illustration while taking into account a particular viewpoint that focuses on the operational context. This work can be seen as a first attempt towards adopting model-driven techniques for assessing and qualifying GNSS-based localization systems for railway operation.

ESREL 2020 PSAM 15, 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, Venise, ITALIE, 01-/11/2020 - 06/11/2020; International audience; Several European actors of railways and GNSS (Global Navigation Satellite System) are committed to develop a safe on-board train positioning system including satellite localization technologies to enable the ETCS (European Train Control System) to manage rail traffic more efficiently. Known risky situations can arise when operating this system, especially when GNSS signals are disturbed. No robust predictive error model exists today to characterize degradations due to local propagation phenomena around the train reception antenna. Different failure detection mechanisms are available to mitigate risks. Nevertheless, such available processes still suffer from safety flaws mainly due to strong model hypotheses on error distributions associated to system measurements. Recent European projects on GNSS have adopted the existing apportionment principles to allocate safety targets, in terms of tolerable hazard rates (THRs), to functions managing feared events due to GNSS. However, the top-down analysis of the allocation process imposes strong requirements for the developed (or in development) dedicated mechanisms. The paper proposes an original method for handling THRs no longer as crisp values but as intervals to take into account both aleatory and epistemic uncertainties of the models used for failure detection and the environmental effect. The proposed allocation methodology based on the common Fault Tree Analysis and interval propagation methods considers model and data uncertainties adding versatility to the allocation method recommended by the standard EN50126.

The growing development of smart objects offers new opportunities for locating the connected traveler. However, tracking the pedestrian's trajectory remains problematic and navigation applications do not propose to track the traveller's trajectory on a multimodal scale autonomously. This work focuses on the implementation of a single solution capable of locating the user according to different travel modes and whatever the environment, using inertial,magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and themovement direction is constrained using inertial signals. These elements were used in a second time and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach which is an inertial navigation technique using dead reckoning is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking direction prediction and to obtain a consistent and smoothed estimate. GNSS measurements are used to correct the velocity vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experiments in real conditions are conducted to analyze the performance of the proposed solution.; Le développement croissant d'objets intelligents offre de nouvelles opportunités de localisation du voyageur connecté. Cependant, le suivi de la trajectoire du piéton reste problématique et les applications de navigation ne proposent pas de suivre la trajectoire du voyageur à l'échelle multimodale de façon autonome. Ce travail s'intéresse à la mise en place d'une solution unique capable de localiser l'utilisateur selon différents modes de déplacement et quel que soit l'environnement, à partir de capteurs inertiels, magnétiques et GNSS. Dans un premier temps, une nouvelle méthode de localisation du cycliste est mise en place. Les mesures de phases GNSS sont utilisées pour corriger le vecteur vitesse par différences temporelles et la direction de déplacement est contrainte à l'aide des signaux inertiels. Ces éléments ont été utilisés dans un second temps et adaptés pour mettre en place une nouvelle méthode de localisation du piéton avec un capteur en main. L'approche PDR qui est une technique de navigation inertielle à l'estime est paramétrée dans un filtre de Kalman étendu. Une mise à jour innovante fusionnant l'estimation de l'attitude du boîtier et une estimation statistique de la direction de marche permet de corriger l'estimation du cap de marche et d'obtenir une estimation cohérente et lissée. Les mesures GNSS sont utilisées pour corriger le vecteur vitesse, l'orientation, la longueur de pas et la position absolue. Enfin, une approche multimodale est proposée et la gestion des transitions entre les différents algorithmes, assistée par l'utilisation d'un capteur innovant, est étudiée. Des validations expérimentales multimodales en conditions réelles sont conduites pour analyser les performances d'estimation de la solution proposée.

The growing development of smart objects offers new opportunities for locating the connected traveler. However, tracking the pedestrian's trajectory remains problematic and navigation applications do not propose to track the traveller's trajectory on a multimodal scale autonomously. This work focuses on the implementation of a single solution capable of locating the user according to different travel modes and whatever the environment, using inertial,magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and themovement direction is constrained using inertial signals. These elements were used in a second time and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach which is an inertial navigation technique using dead reckoning is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking direction prediction and to obtain a consistent and smoothed estimate. GNSS measurements are used to correct the velocity vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experiments in real conditions are conducted to analyze the performance of the proposed solution., Le développement croissant d'objets intelligents offre de nouvelles opportunités de localisation du voyageur connecté. Cependant, le suivi de la trajectoire du piéton reste problématique et les applications de navigation ne proposent pas de suivre la trajectoire du voyageur à l'échelle multimodale de façon autonome. Ce travail s'intéresse à la mise en place d'une solution unique capable de localiser l'utilisateur selon différents modes de déplacement et quel que soit l'environnement, à partir de capteurs inertiels, magnétiques et GNSS. Dans un premier temps, une nouvelle méthode de localisation du cycliste est mise en place. Les mesures de phases GNSS sont utilisées pour corriger le vecteur vitesse par différences temporelles et la direction de déplacement est contrainte à l'aide des signaux inertiels. Ces éléments ont été utilisés dans un second temps et adaptés pour mettre en place une nouvelle méthode de localisation du piéton avec un capteur en main. L'approche PDR qui est une technique de navigation inertielle à l'estime est paramétrée dans un filtre de Kalman étendu. Une mise à jour innovante fusionnant l'estimation de l'attitude du boîtier et une estimation statistique de la direction de marche permet de corriger l'estimation du cap de marche et d'obtenir une estimation cohérente et lissée. Les mesures GNSS sont utilisées pour corriger le vecteur vitesse, l'orientation, la longueur de pas et la position absolue. Enfin, une approche multimodale est proposée et la gestion des transitions entre les différents algorithmes, assistée par l'utilisation d'un capteur innovant, est étudiée. Des validations expérimentales multimodales en conditions réelles sont conduites pour analyser les performances d'estimation de la solution proposée.

2020 IEEE Intelligent Vehicles Symposium, LAS VEGAS, ETATS-UNIS, 20-/10/2020 - 23/10/2020; Over the last decade, the number of accidents involving Vulnerable Road Users (VRU), i.e. pedestrians, cyclists and motorbike drivers, has not decreased in the same way as accidents between passenger cars have. Cooperative systems based on Vehicle-to-X (V2X) communication make it possible to directly exchange information between VRUs and vehicles and to increase the overall situational awareness beyond the capabilities of on-board ranging sensors. To detect and avoid collisions, vehicles require up-to-date and precise information on the location and trajectory of VRUs. In this paper, we propose a VRU localization system based on Global Navigation Satellite System (GNSS), inertial sensors and ultra-wideband (UWB) round-trip-delay ranging technology. We present an exhaustive measurement campaign comprising pedestrians, cyclists and vehicles performed in an urban setting and show first results on the localization performance for a pedestrian crossing an intersection. In the experiments, the pedestrian inertial system supported by GNSS and UWB ranges is able to achieve 0.65m 1\sigma-position accuracy.

Groupede Travail GNSS et Positionnementde la CNIG, Bouguenais, FRANCE, 16-/10/2020 - 16/10/2020; Groupe de Travail GNSS & Positionnement du CNIG (conseil national de l'information géographique).

ION ITM 2020, International Technical Meeting of Institute of Navigation, San Diego, ETATS-UNIS, 21-/01/2020 - 24/01/2020; GNSS used as a standalone positioning system fulfils most of the requirements for many applications since decades. However, the need of high available, accurate and fail-safe positioning systems for new applications such as rail autonomous vehicles for train signaling applications motivates the community to explore novel solutions. In this context, multi-constellation multi-frequency GNSS receivers have the potential to enhance the positioning solutions. However, when applied to safety-critical land transportation system, the localization function not only needs to meet accuracy requirements, but more importantly, it also needs to bound the positioning errors in order to reduce the risk of unexpected and undetected faults. Thus, in order to reach continuous and fail-safe positioning solutions, the use of complementary heterogeneous sensors with a smart hybridization becomes essential. For safety-related and regulated applications as in railways, one will also need, as a next step, to develop the capacity of assessing the safety of the hybrid solution. The concept of integrity can help to reach this requirement and is currently investigated for rail applications. RAIM (Receiver Autonomous Integrity Monitoring) was first defined for aviation applications and is widely used for their safety assessment. However, due to the different safety requirements and operational environment in aviation and land applications (i.e. railway and road application), dedicated algorithms need to be developed for such applications. Indeed, terrestrial applications can face particularly harsh environments (urban canyon, forests ...) in which a dilemma appears between the (reduced) availability of visible satellites and the possibility of encountering multiple simultaneous errors (NLOS, multipath interferences ...). Under these conditions, implementing a strategy based solely on a FDE layer can lead to a decrease in the availability of the localization function. Conversely, relying solely on a strategy of weighting observation error models cannot constitute an acceptable solution from a safety point of view. Only a harmonious combination of these two strategies in a stringent environmental condition can achieve the availability and security requirements. With a real dataset collected along a railway line, we compare the positioning accuracy of a GNSS/IMU tightly coupled system before and after weighting and FDE schemes. The results show that the average value of Horizontal Position Error (HPE) has been reduced after applying the performance improvement method on the GNSS/IMU system. It also shows that the implemented Horizontal Protection Level (HPL) correctly bounds HPE.

The ERSAT-Galileo Game Changer research project, which concluded last November, has laid the foundations for GNSS satellite location technology to support train positioning on secondary railways, potentially reducing the cost of ERTMS deployment.This paper summarizes the main contributions of the project and the perspectives.

La surveillance de santé structurale, ou structural health monitoring (SHM), a pour objectif de caractériser et suivre au cours du temps le comportement et les performances de structures de génie civil. Le SHM repose sur l'établissement d'indicateurs de santé ou de performance permettant de détecter, et si possible localiser, quantifier ou prédire un endommagement de la structure. Aujourd'hui, le SHM exploite encore très peu les déplacements absolus d'une structure, la majorité des capteurs étant limités à des mesures locales ou relatives. Le Global Positioning System (GPS) est accessible au grand public depuis l'an 2000. Il a depuis été rejoint par d'autres systèmes aux principes similaires, formant les Global Navigation Satellite Systems (GNSS), qui offrent des solutions de positionnement absolu pouvant atteindre une précision millimétrique. Cette thèse prend place dans le contexte où les GNSS apparaissent comme de bons candidats pour compléter des instrumentations SHM traditionnelles. Des résultats d'études récentes sur des solutions GNSS à bas coût (antennes de petite taille, récepteurs monofréquence) ont motivé l'orientation des travaux sur l'utilisation de ce type de solutions. Le compromis effectué sur les performances matérielles est contrebalancé par l'utilisation d'un traitement spécifique et par la possibilité de déployer plus de capteurs sur un ouvrage pour un investissement identique. Ces travaux se concentrent sur les structures de grande taille, comme les ouvrages d'art ou les immeubles de grande hauteur, dont le suivi est complexe en raison de leurs dimensions, et qui sont plus à même de produire des déplacements observables par des stations GNSS. La finalité des travaux entrepris est de répondre à la question suivante : peut-on, et comment, utiliser des stations GNSS à bas coût pour détecter des anomalies ou un changement sur une structure de génie civil ? La première partie de cette thèse a été consacrée à l'évaluation expérimentale de solutions GNSS à bas coût. Différentes combinaisons d'antennes et de récepteurs ont été évaluées en utilisant un positionnement relatif sur la phase, afin de sélectionner une station efficiente. En parallèle, l'impact de plusieurs paramètres de calcul ont été évalués dans des scénarios expérimentaux fixes et dynamiques afin de proposer une solution GNSS, comprenant matériel et traitement, optimale. Un positionnement infracentimétrique est obtenu avec la solution sélectionnée. La seconde partie de la thèse considère les données issues de l'instrumentation de deux ouvrages d'art par des réseaux de capteurs GNSS intelligents, les Geocubes, dont les composantes sont similaires à celles évaluées dans la première partie. L'analyse des séries par comparaison et corrélation aux températures et gradients internes des structures a permis de valider la capacité des stations déployées à suivre la réponse des ouvrages aux effets environnementaux. Leur étude a également mis en avant la redondance des observations grâce à l'étude des corrélations plus ou moins fortes entre les séries GNSS acquises. La dernière partie de la thèse est consacrée à l'utilisation de modèles prédictifs des séries GNSS pour la détection d'anomalies sur ouvrage. En l'absence de données thermiques et de modèles mécaniques complets et disponibles pour les structures étudiées, l'approche des modèles par apprentissage exploitant les données d'un réseau de capteurs GNSS a été choisie. La comparaison de plusieurs outils de régression a permis de sélectionner les réseaux de neurones récurrents (RNN) comme outil privilégié. Enfin, une stratégie de détection des anomalies, exploitant plusieurs modèles prédictifs pour chaque série temporelle, a été proposée et testée sur des anomalies réelles observées, puis simulées. Structural health monitoring (SHM) aims to characterize and monitor the behaviour and performance of civil engineering structures over time. SHM relies on the definition of health or performance indicators in order to detect, and if possible locate, quantify or predict damage on the structure. Nowadays, SHM still makes little use of the absolute displacements of a structure, as the majority of the deployed sensors are limited to local or relative measurements. The Global Positioning System (GPS) has been fully available publicly since 2000. It has since been joined by other systems with similar characteristics, forming the Global Navigation Satellite Systems (GNSS), which can achieve absolute positioning with an accuracy up to a few millimeters. This PhD thesis takes place in a context where GNSS solutions appear as good complementary tools to traditional SHM instrumentations. Results of recent studies using lowcost GNSS solutions (small antennas, single frequency receivers) have motivated this research work to focus on similar hardware. The decrease of hardware performance is offset by using some specific processing parameters, and by the possibility to deploy more sensors on a structure for a similar cost. This work focuses on large structures such as bridges, dams, or high-rise buildings, whose monitoring is complex due to their dimensions, and which are more likely to have displacements that can be detected with GNSS stations. The purpose of this work is to answer the following question: is it possible, and how, to use low-cost GNSS stations to detect abnormalities or changes in a structure's behaviour? The first part of this thesis is dedicated to the experimental assessment of various low-cost GNSS solutions. Multiple combinations of antennas and receivers have been evaluated using relative phase positioning in order to select an efficient station. In parallel, several processing parameters have been evaluated in fixed and dynamic experimental scenarios, and an optimal GNSS solution, including hardware and processing, was selected. This solution is able to achieve sub centimeter accuracy. In the second part, datasets from two civil engineering structures equipped with smart GNSS sensor networks are studied. The components of the deployed sensors, the Geocube, are similar to those evaluated in the first part. Time series analysis through comparison and correlation with temperature data validated that the on-site low-cost GNSS stations are able to monitor the effect of environment on structural response. This study also highlights the redundancy of observations with various levels of correlations between the GNSS time series. The last part of this research is devoted to the use of predictive models of on-site GNSS time series for novelty detection. In the absence of complete thermal data or mechanical models available for the investigated structures, an approach using machine learning models with data from multiple sensors of a GNSS network was chosen. The comparison of several regression tools led to select recurrent neural networks (RNN) as the optimal tool. Finally, an automated novelty detection strategy, exploiting several predictive models for each sensor, was proposed and tested on both real and simulated anomalies.

This paper provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (MAitrise de la localisation INdoor), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (PEdestrian Reference SYstem), are mounted on different parts of the subject's body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training centre of firefighters CFIS (Fire and Rescue Training Centre) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the Directorate General of Armaments (DGA: Direction Generale de l'Armement). Provided with raw data, they allow the evaluation of the positioning performances. This dataset is available on the data repository https://doi.org/10.5281/zenodo.4290789 .

47ème Congrès ATEC ITS France, PARIS, FRANCE, 22-/01/2020 - 23/01/2020; Le domaine de la géolocalisation s'est doté d'une méthodologie de référence indispensable pour évaluer et caractériser des terminaux GNSS destinés à la navigation terrestre. La série de normes EN16803 a pour objectif d'éclairer les choix des utilisateurs et de simplifier l'intégration des technologies satellitaires aux systèmes embarqués. Elle s'appuie sur une technique de métrologie instrumentale, identifiée sous le terme « Record & Replay » et offrant plusieurs avantages-clefs : - Restituer des environnements d'essais « Reproductibles » et « Représentatifs » du monde réel ; - Dissocier les erreurs de « Justesse » et de « Fidélité » pour enrichir les analyses ; - Etablir des scénarios de référence fiables pour « comparer les résultats de mesures ».

L'utilisation des GNSS (Global Navigation Satellite System) dans le milieu ferroviaire est d'ores et déjà une réalité sur le terrain. Mais les défis pour l'intégration des technologies de positionnement par satellites dans des systèmes complexes de contrôle-commande et de gestion du trafic ferroviaire restent nombreux.

This deliverable is the second report delivered by GATE4Rail WP3. The general objective of WP3 was to define a comprehensive methodology and the associated tools able to characterise the Global Navigation Satellite System (GNSS) performance into the railways application scenarios defined in WP2 in the framework of a VB detection in ERTMS (European Railway Traffic Management System)/ETCS (European Train Control System) context.The work performed in WP3 is focused on the definition of the main railway environments that will impact GNSS reception, thus PVT performance. This deliverable D3.2 in particular, defines which are the key hazards (both local and global) that have to be taken into account and simulated in order to cover the main events that can occur during a railway operational scenario and how error models will be introduced in the simulation framework defined by the project.

UCP, 3rd edition of the User Consultation Platform, Virtuel, , 01-/12/2020 - 02/12/2020; Comité européen de normalisation des normes GNSS pour la route et le véhicule autonome. Avantages de l'utilisation de la norme EN16803 pour le secteur automobile. Présentation.

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo-ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 degrees in texting mode and 12 degrees in pocket mode.

WCRR 2019, 12th World Congress on Railway Research, Tokyo, JAPON, 28-/10/2019 - 01/11/2019; The use of GNSS positioning into the ERTMS/ETCS has been identified as the technology for supporting the detection of Virtual Balises (VB). However, the presence of local GNSS threats (e.g. multipath, non-line-of-sight (NLOS) or interference) that may not be detectable by a local or wide area augmentation network, can potentially lead to unbounded errors in the pseudorange measurement and ultimately to an error in the VB position that cannot be bounded with the required integrity. In order to prevent a priori the risk of this hazardous situation, VB must be located in areas there are no local threats that may lead a Virtual Balise Reader (VBR) on board the train to dynamically estimate unbounded virtual balise position errors. The ERSAT-GGC project has been funded to tackle with this challenge. This paper aims to present the techniques and methods developed to characterize track areas to know a priori which track portions are suitable for placing VBs. They will rely on basic equipment that every stakeholder shall be able to install and use, composed of a COTS GNSS receiver providing raw data and, when necessary, a spectrum analyzer. The paper describes and illustrates the selected techniques.

ITSC 2019, IEEE Intelligent Transportation Systems Conference, Auckland, NOUVELLE-ZELANDE, 27-/10/2019 - 30/10/2019; The ERSAT GGC project introduces the concept of virtual balises for train localization, which avoids investment and maintenance costs of physical balises. Since this concept relies on the matching of train positions to balise positions stored in a database, it is dependent on placing virtual balises in track areas with unimpeded GNSS reception. One factor majorly contributing to the distortion of GNSS signals is the non-line-of-sight (NLOS) scenario where the direct path between a satellite and the receiver on the train is blocked. As these NLOS situations result in deflections or the total absence of GNSS signals, this paper proposes a system to identify obstacles occluding the visibility of satellites above the tracks traversed by a train. This is achieved by video recording the sky from the roof of the train and segmenting the images into sky and non-sky regions. The line-of-sight status of individual satellites is found through projecting the known satellite locations into the segmented images. Consequently, the information whether a satellite is located in a sky or non-sky segment of the image allows for a determination of the GNSS performance at any observed track area.

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), PISE, ITALIE, 30-/09/2019 - 03/10/2019; With the highly development of navigation techniques during the past decades, the demand for seamless indoor-outdoor navigation is growing from different application fields especially for the military or the first response emergency services. For military applications, one of the key performance requirements is the availability of the positioning solutions for all kinds of dynamics in different environments. Furthermore, due to the stealth requirement in some military actions, it is impossible for military vehicles or personnel to emit signals which enable to be detected by their opponents. This limitation prevents the use of infrastructure-based cooperative localization techniques. The research work of this paper aims at facing the following challenging issues: firstly, to design a positioning filter which is adaptive to the dynamic changes between walking and driving; secondly, to find an approach that correctly identifies the transition between outdoor and indoor with reduced latency; finally, to construct a loosely coupling GNSS/IMU scheme which takes into account the GNSS signal distortion in indoor and urban spaces. Under this context, we propose a complete indoor-outdoor infrastructure-free positioning prototype including a foot-mounted reference navigation system named Pedestrian Reference System (PERSY) and a Ublox High Sensitivity GNSS (HSGNSS) receiver (M8P). A loosely-coupled architecture between GNSS receiver and the PERSY is employed by using an indicator of horizontal position accuracy $\backslash$textitPACCH provided by the GNSS Ublox M8P receiver. This indicator allows qualifying the position solutions delivered by the GNSS receiver as well as detecting the transition of indoor/outdoor, which helps the PERSY to update with absolute positions from GNSS. This positioning prototype can take advantage of both GNSS and PERSY so as to realize a seamless indoor-outdoor positioning for pedestrians and vehicles. The proposed system is evaluated in two scenarios over respectively 2.17 km and 2.68 km including indoor , outdoor and in-vehicle phases. The median horizontal position errors for the two scenarios are respectively 2.23 m and 1.93 m.

59e congrès annuel du club EEA , RENNES, FRANCE, 12-/06/2019 - 12/06/2019; Présentation d'actions IFSTTAR sur le déploiement des véhicules autonomes, aspects industrialisation, recherche et enseignement.

2019 16th Workshop on Positioning, Navigation and Communications (WPNC), BREME, ALLEMAGNE, 23-/10/2019 - 24/10/2019; Since the availability of GNSS raw measurements with Google Nougat API in 2016, research has been assessing smartphone performances and GNSS data's quality. The objective is to achieve precise positioning and to assess its quality. With the growing Internet of Things (IoT), embedded sensors spread everywhere for acquiring, assessing and monitoring the environment, for which low-cost and precise positioning is essential. Low-cost does not only relate to the financial cost of the GNSS receiver but also to other costs like data consumption, battery consumption and computation cost. Using the Google API and the new generation of smartphones, these costs can be targeted to produce smarter, more efficient, and optimized positioning solutions for being implemented on other IoT devices. This paper presents research on Real-Time Precise Point Positioning (PPP-RTK) application development in Android. Analysis of needed RTCM (Radio Technical Commission for Maritimes Services) streams is given along with the assessment of their benefits for efficient positioning. Focus is made on evaluating the cost of precise products in terms of internet data consumption.

WCRR 2019, 12th World Congress on Railway Research, Railway Research to Enhance the Customer Experience, Tokyo, JAPON, 28-/10/2019 - 01/11/2019; Innovative technologies enable new approaches for train control, command and signaling systems. E.g. digitalization and automation lead to completely new concepts. The partner RAILENIUM, NSL, CEIT and DLR have founded the consortium Smart Rail Control (SmartRaCon) to develop new concepts, approachs and technologies for the train control, command and signaling systems of the future. In this contribution the developments in the areas of communication, localization and train integrity will be discussed. For the communication the next generation of mobile radio 5G is in the focus with the capability to support software defined networks and radio as well as virtualization potentials. For localization and train integrity supervision algorithms for the fusion of sensor data and digital maps are the key elements. E.g. Global Navigation Satellite Systems (GNSS) together with complementary sensors can be used to achieve a safe and reliable localization. This concept again can serve as a core of a end-of-train device for train integrity monitoring. Further elements like the traffic management or moving block as well as procedural aspects like simulation-based testing are subject of the SmartRaCon concepts and are related to the issues discussed here. The architectural concept to integrate all those elements is based on the idea to use components and software common-off-the-shelf (COTS) and to integrate them into a railway network in a modular way.

10th Chinese Satellite Navigation Conference, BEIJING, CHINE, 22-/05/2019 - 25/05/2019; Over the past 15 years, there has been an exponential growth of new technologies for indoor positioning and navigation. Unlike GNSS technology, which has become the leading solution for outdoor positioning, no technology has taken the lead indoors. With the price drop of radio beacons, we see a massive deployment of beacons' networks for positioning. Image processing is progressing fast thanks to machine learning techniques that improve the rendering of very low-cost cameras. More and more smart devices embed inertial sensors providing autonomous navigation options. These are only a few of the technologies deployed. Hybridizing technologies to find the best compromise between accuracy, cost and energy consumption is at the heart of ongoing development. The nature of sensors in the infrastructure or in smart devices, specific use cases requirements and privacy concerns about geolocated data are all features used to choose the right technologies to hybridize. Adopting a ubiquitous approach that combines dead reckoning and absolute positioning while recognizing the application and environmental context is certainly a strong trend in current developments and research. Given the great diversity of existing positioning systems and ways of presenting their performance, it seems almost impossible to provide a clear comparison of localization performance. The key to this comparison certainly lies in experimental comparative trials, in the same context and on identical scenarios. This approach started several years ago with international positioning competitions.This talk will review main indoor positioning technologies according to several comparison criteria. It will also exploit the results of last Indoor Positioning Indoor Navigation (IPIN) international competition that took place in a 9'000 m² shopping mall (Atlantis) in Nantes (France) where 49 teams competed.

WCRR 2019, 12th World Congress on Railway Research, Tokyo, JAPON, 28-/10/2019 - 01/11/2019; New technologies such as GNSS-based positioning solutions will contribute to the modernization and efficiency enhancement of the railway system. Its capacity to provide a global absolute position without any infrastructure to be supported by the user at reasonable cost makes GNSS a game changer for rail. However, in order to reach the high-level performance requirements needed to apply these technologies to safety-critical applications, and in particular, for the use of virtual balises, some solutions need to ensure their accuracy, robustness and safety. Coming from aeronautics experience, the RAIM (Receiver Autonomous Integrity Monitoring) can help reducing inaccuracy by detecting and excluding faulty measurements and bounding residual errors. In this paper, we intend to demonstrate how the combination of weighting schemes and FDE (Fault Detection and Exclusion) can help the system to enhance GNSS performance in a railway environment where multipath, non-line of sight signals and interferences can occur. Some results based on real data are presented in order to quantify the benefit. Application of such techniques will help designing future on-board location units by evaluating their potential performance before deciding the level of hybridization that will be mandatory to ensure continuity of service and compensate the drawbacks of GNSS in harsh environments.

2019 16th Workshop on Positioning, Navigation and Communications (WPNC), BREME, ALLEMAGNE, 23-/10/2019 - 24/10/2019; Since the availability of GNSS raw measurements with Google Nougat API in 2016, research has been assessing smartphone performances and GNSS data's quality. The objective is to achieve precise positioning and to assess its quality. With the growing Internet of Things (IoT), embedded sensors spread everywhere for acquiring, assessing and monitoring the environment, for which low-cost and precise positioning is essential. Low-cost does not only relate to the financial cost of the GNSS receiver but also to other costs like data consumption, battery consumption and computation cost. Using the Google API and the new generation of smartphones, these costs can be targeted to produce smarter, more efficient, and optimized positioning solutions for being implemented on other IoT devices. This paper presents research on Real-Time Precise Point Positioning (PPP-RTK) application development in Android. Analysis of needed RTCM (Radio Technical Commission for Maritimes Services) streams is given along with the assessment of their benefits for efficient positioning. Focus is made on evaluating the cost of precise products in terms of internet data consumption.

This document is the deliverable D4.2-Technical Specification of the Survey Toolset where the outcome of the work performed in WP4.2 of the ERSAT GGC project is described. This document follows up the work performed in WP4.1 and reported in the deliverable D4.1, extending the ongoing general definition of the track area classification process. The goal of WP4.2 is the specification and development of the toolset for the survey and classification of track areas, giving the system overview and providing the different interacting blocks as the measurement hardware system, the processing software toolbox and the final classification data. Regarding this D4.2, the functional and technical specifications of the toolset are here provided with the description of the interfaces between the different detection techniques and decision logic blocks. Outcome of D4.3 will be the prototype implementation and installation. The document also includes the specification of the measurement system and hardware to be used in the survey process so that the necessary data for the classification techniques can be collected in the railway scenario. Finally, the specification of the output data - the track area classification results - is also analysed and provided. The specification of the survey measurement system and software toolset addressed in the current document will support the next project activities related to the development of the software tools for the classification of track areas in WP4.2 and the execution of measurement campaigns in WP4.3.

This document is the deliverable D4.3-Prototype Implementation of the Survey Toolset, which provides a high level description of the Toolset Prototype. This document is released in a pre-final version, where the toolset implementation has been done using demo data coming from RFI-ASTS private campaign in Italy. The dataset, related to the Pinerolo-Torino line, was used to test and debug the toolset, and the results provided in this document refer to that line and campaign. The Consortium thanks RFI, ASTS and TRI for the investment done to this aim and the availability of needed dataset in order to respect the scheduling of the project. The toolset will be definitively designed after the test campaigns on field, which will confirm the assumptions and in particular the decision logic approach. In fact, once the test campaigns foreseen in the ERSAT GGC will be performed - as planned in the WP4.3 task - the final implementation of the toolset prototype will be released based on those data. A conclusive issue of this deliverable will be then provided with the results of the test campaigns.

This document reports the execution of the track area classification process developed during the work package « WP4 Track Survey and Track Classification » along with the main outcomes and results obtained during this process. The goal of WP4 is to set up a standard measurement and classification system that is able to provide a prediction of the expected presence of GNSS hazard causes and degraded performance to be expected at a given track area, in order to enable the evaluation if the specific environment is suitable or not to place virtual balises or on the contrary should be discarded beforehand. In this document, we detail the results of applying the classification standard process, by means of the developed software Toolset to the measurement data collected in the three measurements campaigns during ERSAT GGC project. This execution report addressed the main important aspects that need to be carried out for the correct application of the classification process and the results obtained with the data collected.

This is the deliverable D4.5 Process Execution Report of the ERSAT GGC project. This document reports the execution of the track area classification process developed during the work package WP4 Track Survey and Track Classification along with the main outcomes and results obtained during this process. The goal of WP4 is to set up a standard measurement and classification system that is able to provide a prediction of the expected presence of GNSS hazard causes and degraded performance to be expected at a given track area, in order to enable the evaluation if the specific environment is suitable or not to place virtual balises or on the contrary should be discarded beforehand. In this document, we detail the results of applying the classification standard process, by means of the developed software Toolset to the measurement data collected in the three measurements campaigns during ERSAT GGC project. This execution report addressed the main important aspects that need to be carried out for the correct application of the classification process and the results obtained with the data collected.

Global Navigation Satellite Systems (GNSS) such as GPS, already used in aeronautics for safety-related applications, can play a major role in railway safety by allowing a train to locate itself safely. However, in order to implement this positioning solution in any embedded system, its performances must be evaluated according to railway standards. The evaluation of GNSS performances is not based on the same attributes class than RAMS evaluation. Face to these diffculties, we propose to express the integrity attribute, performance of satellite-based localisation. This attribute comes from aeronautical standards and for a hybridised GNSS with inertial system. To achieve this objective, the integrity attribute must be extended to this kind of system and algorithms initially devoted to GNSS integrity monitoring only must be adapted. Thereafter, the formalisation of this integrity attribute permits us to analyse the safety quantitatively through the probabilities of integrity risk and wrong-side failure. In this paper, after an introductory discussion about the use of localisation systems in railway safety context together with integrity issues, a particular integrity monitoring is proposed and described. The detection events of this algorithm permit us to conclude about safety level of satellite-based localisation system.

Pour assister la navigation piétonne dans les espaces urbains et intérieurs, une estimation précise de la pose (i.e. la position 3D et l'orientation 3D) d'un équipement tenu en main constitue un point essentiel dans le développement d'outils d'aide à la mobilité (e.g. applications de réalité augmentée). Dans l'hypothèse où le piéton n'est équipé que d'appareils grand public, l'estimation de la pose est limitée à l'utilisation de capteurs à faible coût intégrés dans ces derniers (i.e. un récepteur GNSS, une unité de mesure inertielle et magnétique et une caméra monoculaire). De plus, les espaces urbains et intérieurs, comprenant des bâtiments proches et des éléments ferromagnétiques, constituent des zones difficiles pour la localisation et l'estimation de la pose lors de grands déplacements piétons. Cependant, le développement récent et la mise à disposition d'informations contenues dans des Systèmes d'Information Géographiques 3D constituent une nouvelle source de données exploitable pour la localisation et l'estimation de la pose. Pour relever ces défis, cette thèse propose différentes solutions pour améliorer la localisation et l'estimation de la pose des équipements tenus en main par le piéton lors de ses déplacements en espaces urbains et intérieurs. Les solutions proposées intègrent l'estimation de l'attitude basée inertielle et magnétique, l'odométrie visuelle monoculaire mise à l'échelle grâce à l'estimation des déplacements du piéton, l'estimation absolue de la pose basée sur la reconnaissance d'objets SIG 3D parfaitement connus et la mise à jour en position de la navigation à l'estime du piéton. Toutes ces solutions s'intègrent dans un processus de fusion permettant d'améliorer la précision de la localisation et d'estimer en continu une pose qualifiée de l'appareil tenu en main. Cette qualification est nécessaire à la mise en place d'un affichage en réalité augmentée sur site. Pour évaluer les solutions proposées, des données expérimentales ont été recueillies au cours de déplacements piétons dans un espace urbain avec des objets de référence et des passages intérieurs. To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3D position and 3D orientation) of an equipment held in hand constitutes an essential point in the development of mobility assistance tools (e.g. Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e. an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements, constitute challenging areas for localization and sensor pose estimation during large pedestrian displacements. However, the recent development and provision of data contained in 3D Geographical Information System constitutes a new wealth of data usable for localization and pose estimation. To address these challenges, this thesis proposes solutions to improve pedestrian localization and hand-held device pose estimation in urban and indoor spaces. The proposed solutions integrate inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation, 3D GIS known object recognition-based absolute pose estimation and Pedestrian Dead-Reckoning updates. All these solutions are fused to improve accuracy and to continuously estimate a qualified pose of the handheld device. This qualification is required to validate an on-site augmented reality display. To assess the proposed solutions, experimental data has been collected during pedestrian walks in an urban space with sparse known objects and indoors passages.

ITST 2018, 16th International Conference on Intelligent Transport Systems Telecommunications, Lisbonne, PORTUGAL, 15-/10/2018 - 17/10/2018; The introduction of the GNSS positioning technology into the evolution of ERTMS/ETCS is based on the concept of virtual balise to minimize the impact onto existing ERTMS solutions. The detection of virtual balises is foreseen by using GNSS in combination with odometry information. However, the presence of local GNSS threats (e.g, multipath, NLOS or interference) may lead to unbounded errors in the pseudorange measurement that cannot be corrected by local or wide-area augmentation systems, and will lead ultimately to an error in the virtual balise position that cannot be bounded with the required integrity. In order to a priori prevent the risk of this hazardous situation, virtual balises must be logically located in areas where there are not local threats that may lead a Virtual Balise Transmission System on board the train to dynamically estimate unbounded virtual balise position errors. This paper summarizes the initial work performed in the H2020 GSA Project ERSAT-GGC with respect to the different techniques that can be used to detect local GNSS threats and that can support a later classification of railway areas as suitable or not suitable for placing virtual balises.

IPIN 2018, 9th International conference on Indoor Positioning and Indoor Navigation, Nantes, FRANCE, 24-/09/2018 - 27/09/2018; This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).

2018 International Conference on Indoor Positioning and Indoor Navigation, Nantes, France, 24-/09/2018 - 27/09/2018; To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3D position and 3D orientation) of an equipment held in hand constitutes an essential point in the development of mobility assistance tools (e.g. Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e. an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements, constitute challenging areas for sensor pose estimation during large pedestrian displacements. However, the recent development and provision of 3D Geographical Information System content by cities constitutes a wealth of data usable for pose estimation. To address these challenges, we propose an autonomous sensor fusion framework for pedestrian hand-held device pose estimation in urban and indoor spaces. The proposed solution integrates inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation and known 3D geospatial object recognitionbased absolute pose estimation. Firstly, this allows to continuously estimate a qualified pose of the device held in hand. Secondly, an absolute pose estimate enables to update and to improve the positioning accuracy. To assess the proposed solution, experimental data has been collected, for four different people, on a 0.5 km pedestrian walk in an urban space with sparse known objects and indoors passages. According to the performance evaluation, the sensors fusion process enhanced the pedestrian localization in areas where conventional hand-held systems were not accurate or available

2018 International Conference on Indoor Positioning and Indoor Navigation, Nantes, France, 24-/09/2018 - 27/09/2018; International audience; In indoor environments, pedestrian dead reckoning (PDR) is the most used strategy for pedestrian position estimation from inertial data collected with handheld devices. PDR process recursively estimates positions using step length estimation based on parametric models that take into consideration some physiological parameters, displacement features and acceleration statistical properties. The coefficients of these models need frequent adjustment to limit cumulative errors induced by alteration of gait pattern. A large experimental database providing information about human locomotion variability is required for this calibration. However, the development of such database is costly in terms of time and effort. To make the collected data as reliable as possible, several gait-affecting factors should be considered, which highly increases the number of measurement trials. In this paper, we propose an alternative way of generating locomotion data that consists in simulating human walking gait motion under different conditions. We propose a multibody system simulator taking into account possible step-level asymmetry induced by handling a device in hand, as well as the correlation between arms and legs motions during gait. Our simulation approach was evaluated with data from overground walking experiments on one test subject. Preliminary results show some similarities between acceleration profiles related to different body parts, and the same variation trends of selected acceleration items in function of carrying mode and gait velocity.