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Nowadays, Global Navigation Satellite Systems are the main source for high accuracy positioning and timing. For this reason, they are essential both for everyday activities and services, and for the industrial and critical infrastructure sectors. Moreover, the spread of increasingly autonomous vehicles results in strict accuracy and integrity requirements. This leads to the need for additional infrastructure to send corrections to the end users and mitigate the measurement errors, the Augmentation Networks. However, due to the increasing exploitation of localization functionalities, the Augmentation Networks could become a primary target for attackers resulting in a high financial and safety cost. Among the possible attacks, spoofing, that is the generation of a fake satellite signal which is seen as genuine by the receiver, is one of the most powerful and tricky. In this contribution, a detection and mitigation strategy for Augmentation Network spoofing attacks is proposed. We introduce two attack models and present a technique based on K-means clustering to counteract them. More in details, our approach is based on the computation of the number of clusters formed by the Augmentation Network corrections. Starting from the hypothesis that under nominal conditions only one cluster is present, the effects of the attacks on the clustering procedure are analyzed, and several attack simulations are performed to evaluate the algorithm performances. The proposed method has been compared both to an Augmentation Network attack detection technique, and to a receiver-level spoofing mitigation approach, showing comparable or better performances. Moreover, to the best of our knowledge, this is the first work addressing mitigation for spoofing attacks which target an Augmentation Network.

One of the major challenges in the design of high accuracy, high integrity localization procedures for rail applications based on Global Navigation Satellite Systems is represented by the local hazards that cannot be mitigated by resorting to augmentation networks. By fact, combining smoothed code pseudoranges with (differential) carrier phase and/or with Inertial Measurement Unit's outputs is ineffective against multipath low frequency components. These issues can be mitigated by processing images, depth maps and/or pointclouds provided by imaging sensors placed on board. The absolute position of the train can be determined by combining its relative position with respect to georeferenced rail infrastructure elements (e.g., panels, signals, signal gantries) provided by the visual localization processing unit with the landmark absolute position. In addition, the visual input can be exploited for determining on which track the train is located and can be used as complementary odometry source. Moreover, the information provided by the visual localization processing unit can be used to monitor integrity and compute the protection levels. In this contribution we present a localization system that integrates a Global Navigation Satellite System receiver, Inertial Measurement Units, and video sensors (such as monocular and stereo video camera, Time of Flight camera and LIDAR), and has the potential to overcome some of the operational and economical limitations of the current train localization system employed in the European Railway Traffic Management System.

This paper presents an innovative global navigation satellite system (GNSS) fault detection and exclusion approach for the adoption of satellite localization in the rail sector. Current global integrity monitoring systems cannot guarantee the safety level needed for such applications as train control where tolerable hazard rate in the order of 10−9/h is required. A new method, named two tiers, enabling to integrate local augmentation systems and global augmentation infrastructures, is presented. It is based on the comparison of single differences residuals among satellites for detecting signal in space (SIS) faults and double difference residuals among local augmentation stations and satellite-based augmentation systems ranging and integrity monitoring stations for detecting reference stations faults. GPS SIS faults described in literature and real GNSS raw data recorded on a train are taken into account. This study reports the performance analysis for the two-tiers approach carried out during relevant European projects. A test-bed architecture has been developed through the implementation of the algorithm in real time on a local augmentation operational center. Relevant performances have been tested on a rail track for validating the algorithm in real operative conditions. Significant results of the analysis are reported for SIS integrity assessment only.

Cyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided.

Current high accuracy positioning systems for transport applications require high safety and high accuracy. Considering that some of the most relevant errors leading to a loss of integrity are multipath and interferences this paper introduces the use of a set of digital maps for mitigating the presence of such hazards. Moreover, relevant statistical modelling of multipath and interference errors is provided. More in details, three maps are introduced: the satellite visibility map, the multipath map and the interference map. The satellite visibility map, through a piecewise constant function, allows the receiver determining, for each azimuth, the minimum elevation of a satellite to consider it suitable for PVT. Moreover, a quantized version of such parameters allows designing an optimized version of the data to be transmitted. Concerning the interference map, it is constituted by a list of the prevailing radio frequency interferences and their spectral characteristics. As for the multipath map, a statistical model where the variance of the multipath error is considered as a random variable with a discrete distribution is considered. More in details, the multipath variance is modelled as the product of a geometrical factor, depending on the satellite elevation, and the C/N0, and a stochastically modeled inflating factor. These assumptions result in a distribution of the multipath error which follows a Gaussian Mixture. Therefore, a Mean Square receiver position solution, and the application of Solution Separation for satellite fault determination and PL calculation under the Gaussian Mixture distribution hypothesis, is derived. A Monte Carlo simulation is carried out for determining the estimation error, with respect to a Weighted Least Square solution and a classical Gaussian distribution of the multipath error with inflating factor equal to 1. Thanks to the better matching of the measurement error distribution, larger errors are attenuated and accounted for by the protection level. A consistent reduction in misleading information is demonstrated through a Stanford Plot analysis.

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.

Interleaved Reed–Solomon codes admit efficient decoding algorithms which correct burst errors far beyond half the minimum distance in the random errors regime, e.g., by computing a common solution to the Key Equation for each Reed–Solomon code, as described by Schmidt et al. If this decoder does not succeed, it may either fail to return a codeword or miscorrect to an incorrect codeword, and good upper bounds on the fraction of error matrices for which these events occur are known. The decoding algorithm immediately applies to interleaved alternant codes as well, i.e., the subfield subcodes of interleaved Reed–Solomon codes, but the fraction of decodable error matrices differs, since the error is now restricted to a subfield. In this paper, we present new general lower and upper bounds on the fraction of error matrices decodable by Schmidt et al.’s decoding algorithm, thereby making it the only decoding algorithm for interleaved alternant codes for which such bounds are known.

Satellite navigation plays a critical role in new railway signaling and train control systems. It will allow higher levels of automation at a reduce cost in installation and maintenance as compared to current infrastructure-based technologies. However, in order to be integrated in railway systems, GNSS must guarantee the stringent railway safety requirements. GNSS safety has been extensively quantified for the aviation community, however, in the railway environment the probability of local GNSS hazards like interferences is larger due to its operation in a non-restricted space. Railway GNSS receivers must be therefore equipped with mechanisms to protect the position determination from being corrupted by undesired interferences (jamming or spoofing) without being aware of it. In this paper and based on Commercial-Off-The-Shelf (COTS) hardware we implemented interference detection functions suitable for railway localization. We detail the algorithm design as well as the necessary calibration procedure to determine the nominal signal model. The performance of the interference detection is analyzed with an experimental setup on a commercial train during the measurement campaign organized for the EU project ERSAT-GGC. Figures about the detection capability and detected events along the selected line are finally provided.

The upcoming transport revolution is dramatically increasing the navigation integrity requirements. However, traditional techniques for increasing the integrity of the position information are usually based on the use of a high number of satellite signals. We propose to add an external integrity source based on the use of on-board visual sensors. Shift and rotation of the vehicle are estimated from the vanishing point of the road markings, and exploited to define a virtual track for its motion. The GNSS-only solution is then compared to the one constrained to the virtual track, and an integrity check is performed based on a threshold. The performed experiments prove the possibility of exploiting the visual system to reliably compute the vehicle’s dynamics from the acquired images, and show the impact of the virtual track constraint on the integrity check.

The GNSS has been selected as one of the main step charge innovation in the land transportation domain. This contribution considers as target users’ railway and automotive applications. As a matter of fact, these two applications share the same electromagnetical environment and both of them are subjected to constrained movement profile. More in details, train signalling, connected cars and UAV for surveillance of roads and railways can take advantage of this technology to realize cost-effective solutions for high integrity and high accuracy positioning systems. One of the objectives is to create a synergic ecosystem among rails and roads sharing digital infrastructures, safety and certification processes to lower energy consumption and pollution. Furthermore, the liaison among these two transportation means is motivated by a market perspective. In fact, the rail stand-alone marketplace for GNSS localizers will never be so large as to motivate volume production while the driverless cars can target to millions of vehicles [1]. The main pillars behind this solution are the implementation of a high-performance Multi-modal Augmentation & Integrity Monitoring Network (AIMN) and a multi-sensor on-board unit (MOBU). The multi-modal AIMN will be design to complement the existing SBAS systems. Furthermore, the number of local stations can be reduced thanks to the synergy between rail and road application. In addition to that, UAVs operations monitoring the roads and railways assets, can take a boost to these applications contributing again to a significant reduction of energy and pollution. Concerning the OBU, a data fusion among several sensors will be used to increase the availability and the accuracy; however, to increase the system integrity, the final solution will be designed by accounting for at least two independent estimation chains. In terms of Key Performance Indicators, land transportation systems require not only high integrity and high accuracy, but also navigation solutions validated and certified according to standardized procedures. The actual trend to fulfil these requirements is to resort to the functional redundancy by exploiting the integration of multiple low-cost Commercial Of The Shelf (COTS) sensors, including GNSS receivers, IMUs, Odometers and speedometers, LIDARs, video cameras, and so on. In terms of performance assessment, at the moment the major role has been spent on the Position domain. Nevertheless, also the Velocity and the Timing will play a central role in the future applications both for rail and for automotive sectors. Particularly, in the railway domain, the Moving Authority (a set of information required for the train to move within the ERTMS system) contains the speed restrictions that the train must follow. The speed information will be crucial in the ERTMS Level3 when the distance among two consecutives convoys will be dynamically allocated under the moving block approach. Concerning the automotive, the velocity estimation (both longitudinal and lateral) are crucial in the overtake procedure (e.g. to estimate if there is enough space to return into the lane after the overtake without colliding with a car occupying the other lane). Having a high integrity and high accuracy velocity estimation will play an important role In all these applications. Beside the speed, the timing is crucial for all the applications related to cooperative navigation like the platooning for example where the synchronization of the PVT solution will be mandatory or when the relative position among vehicles will be used. A major role in the framework of high integrity and high accuracy land transportation positioning systems is played by the requirements definition. Particularly, each application will have a set of specific user requirements expressed both in term of functional and non-functional requirements. This requirement will be retrieved according to the use cases taken from the standards (like ERTMS, SAE and CENELEC norms). The first challenge to realize the multi-modal platform is to provide a harmonized set of requirements. The first aim of this work is to lay the basis for a shared framework among railway and automotive requirements mapping them from the uses cases to the system requirements. In this work different categories of requirements will be considered such as: functional, performance, and interface. Particular emphasis will be given in the safety case study. The apportionment of the requirements from system to the subsystem level will be derived through a fault tree analysis. In the full paper, the full process from the user requirements collection to the system and sub-system level requirements definition will be described. Particularly, the selection of the use cases and operational scenarios from the norms and from the state-of-the-art documentation will be described. The user requirements will be collected and described as well as their harmonization exploiting the synergies among rail and road will be shown. After that, the mapping of user requirements into system requirements will be given and the apportionment of requirements from system to the subsystem level will be shown through the fault tree analysis. Finally, from the obtained set of requirements a candidate architecture solution will be provided. [1] Salvatori, Pietro, Stallo, Cosimo, Coluccia, Andrea, Pullen, Sam, Lo, Sherman, Neri, Alessandro, "An Augmentation and Integrity Monitoring Network for Railway and Automotive Transportation," Proceedings of the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, January 2019, pp. 790-801. https://doi.org/10.33012/2019.16724

Before deployment and commissioning of new positioning products/applications, manufacturers need to perform long and often expensive field trials in targeted operational environments in order to assess and certify future behaviour. The community trust on GNSS (Global Navigation Satellite System) is so high that more and more safety critical applications based on satellite navigation are under development. With the increase of criticality level of GNSS applications, a relevant means to predict the performances and reliability of future applications is needed. Thus in this paper we introduce a new architecture based on Machine Learning that combines classical observables for local hazard detection, with the outcome of advanced RAIM in order to determine whether a given point of a railway is suitable for a safe and reliable use of GNSS for train positioning.

The objective of this work is to evaluate the feasibility of the integration of visual sensors in the framework of GNSS-based vehicle localization systems. Undoubtedly GNSS plays a key role in vehicle localization. However, the impact of GNSS faults (e.g. satellite and constellation failure), signal degradation (e.g. ionospheric scintillations, multipath) and external threats (e.g. jamming and spoofing) need to be taken into account. To address this issue, we propose a three-level localization framework based on the use of additional visual sensors. Thanks to the visual input, the inaccurate GNSS measurements are refined by tracking georeferenced landmarks to obtain the vehicle absolute position. Moreover, the same sensors can be used to obtain the relative position and motion of the vehicle. Every time a new group of landmarks is detected, it is possible to repeat the absolute positioning process to cancel the drift accumulated by the relative localization procedure. This work represents the first step towards the realization of the proposed localization technique. Initial testing to evaluate its suitability has been carried out and is reported hereafter.

Here, we investigate PVT solutions based on multi-constellation receivers and dedicated augmentation networks allowing to determine the track on which a train is operating with a very high safety Integrity level. Because the time needed to accomplish this task has a relevant impact on the rail traffic management performance and, therefore, it is considered a key performance indicator by rail infrastructure managers, a double frequency solution is investigated. Particularly, we focus our attention on the advantages of relative positioning solutions, based on Double-Difference Wide-Laning Carrier Phase measurement combination, exploiting the fact that the train location is completely determined by its mileage due to the track constraint, to speed up track discrimination. No ambiguity fix is required at this scope. Here, a detailed description of the overall processing and achievable performance is given. Performance assessment is provided by means of Monte Carlo simulations based on observations recorded on field campaigns.

The ERTMS (European Train Management System) has been developed by the European Union (EU) to enhance safety, increase efficiency and to cross-border interoperability creating a unique solution fulfilling a standardized certification process. The ERTMS being able to automatically stop the train to overcome human errors has achieved the highest track record in terms of safety over several billion km travelled each years. GNSS positioning, bearer-independent telecoms and ATO (Automatic Train Operation) are the new features for enhancing the ERTMS in the path to fully autonomous operations. In the same period, the automotive industry has launched ambitious plans for the connected cars and autonomous driving applications are emerging as the next wave of innovation. This paper evaluates the challenges to sharing intelligent infrastructure means, by combining the strengths of the safety benchmark achieved on the rail transport with the mass production capability of the automotive industry to lower the costs. In this scenario, rail and automotive becoming tightly intertwined can get a grip in the race towards a fully automation affordable and safe, giving birth to autonomous vehicles able to travel within virtual rails as “trains” on the road. To this aim we will introduce the two test bed in Italy respectively for validating the ERTMS with GNSS positioning and public telecoms networks and for testing FCA Ducato vans to operate at SAE level 3 automation exploiting the new GALILEO and 5 G services.

The European train control system employs GSM-R for communications between trains and the command/control centers. GSM-R technology is quite old and will be replaced in the near future by other technologies offering less expensive solutions, especially for regional and local lines. This article investigates a new telecommunication solution based on multi radio bearers using cellular and satellite public networks as an alternative to the deployment of a dedicated infrastructure. A test campaign on a 300 km of rail line (about 10,000 km of tests) has been carried out to evaluate the performance of cellular and satellite networks in a railway environment. Test trial results presented and discussed in this article have been used to assess the performance of a multipath TCP protocol to effectively support multi-bearer communications. A discussion of the economic benefits and the potential industrial implications of the proposed solution concludes the article.

In this contribution, a watermarking system for omni-directional images is presented. To cope with the peculiarities of this media, an ad-hoc watermarking technique has been designed. In particular, since the users can only view a small portion of the 360° images, the viewport, the watermarking procedure has been designed for allowing the extraction of the watermark even from a single viewport. In this way it is possible to protect the ownership of the content even if only a viewport is illegally distributed. The experimental results show that the proposed system is robust against several kinds of attacks, outperforming the current state-of-the-art in omni-directional image watermarking.

Vehicle’s positioning with high accuracy, integrity and continuity is a mandatory requirement for Automated trains, Connected cars and Driverless metros. Even if GNSS positioning is being introduced for train and car automation in conjunction with other sensors its availability is not guaranteed everywhere as inside tunnels and in cutting edge areas. The objective of our paper is to investigate the Fine Timing Measurement (FTM) function recently introduced in the IEEE 802.11 standard for the determination of the distance between pairs of stations for locating vehicles in GNSS denied areas. This solution can be extended also to the metros where this kind of networks is used for the communication between the trains and control center. We combine the use of the RTT of signals exchanged by a vehicle equipped with a Wi-Fi unit and a Wi-Fi beacon supporting the Fine Timing Measurement protocol with the physical or the virtual track constraint to overcome the limitation of positioning based on Round- Trip Time (RTT) which implies that measures with respect to different beacons are sequentially acquired. A reference architecture applicable to train, cars and metros is presented and the localization performance are simulated with the Monte Carlo tool. This approach can be used to complement the GNSS positioning in areas where the signals are not available or affected by interferences and in the case of metros to reuse the Wi-Fi network for assessing the train positioning. This work has been developed under the grant of the “ERSAT GGC” project of the H2020-GALILEO-GSA-2017-1 Program (project number 776039).

In the last years, several studies have addressed the introduction of the GNSS technology in the framework of the high integrity positioning methods for the land transportation. The main emphasis has been spent for the rail environment. The main driver is the possibility to realize cost effective solution for the modernization of the lines without losses in terms of safety. Furthermore, the rail community have recognized GNSS as one of the step charge innovation foreseen in the next generation train control systems. In the recent period, there has been some attempts to exploit the outcome of such studies even in the driverless car applications. As a matter of fact, these two transportation means share the same electromagnetical environment and both of them are subjected to constrained movement profile. Since one of the most important KI in the safety related application is represented by the integrity, we considered the presence of an external augmentation and integrity monitoring network. Particularly, we considered three different approaches for augmenting the PVT solution that are respectively the local area augmentation network, the wide area augmentation network and the 2-tiers augmentation schemes. For all these schemes we carried out the SWOT analysis considering the peculiarities of the application. On the basis of the SWOT, the system integrator can decide to implement the network that better suit its own requirement. In or vision, the best solution is to share a unique augmentation network for both rail and connected cars. In this way it is possible to increase the number of user served by the network itself.

This work aims to a better understanding of augmentation network security. In more details, the objective is to simulate a spoofing attack aimed at introducing displacements in the positions computed by rovers that make use of an augmentation service. At first, we investigated the features of such an attack. Then, after introducing the Local Area Augmentation System presented in [1], we evaluated its vulnerabilities. At this aim we generalized the methodology introduced in [1] in order to evaluate the effects on the rovers of faults affecting the Reference Stations employed by the Augmentation Network to the case in which the errors affecting the pseudoranges, measured by different stations, are correlated, as in case of coordinated spoofing. The analytical evaluation is complemented by numerical results obtained by a GNSS simulator, fed with data recorded by a reference station network deployed in the Sardinia Island (Italy), in order to assess the feasibility of a train control system integrating GNSS technologies.

Designs and results for a low-power and economical fingerprint visible light positioning (VLP) system are discussed in this paper. A system using four white LEDs and one photodiode was deployed. The LEDs are independently controlled and modulated by individual transmitter circuits, removing the requirement for a large, expensive signal generator. The receiver circuit filters and converts the received signal to DC, allowing a simple microcontroller to save the received signal. We propose the creation of a fingerprint database by recording signal data, fitting a two-dimensional Gaussian distribution to the data, and then generating the fingerprint database for all positions in the evaluation system. System positioning accuracy of 13.44 + / − 0.36 mm was observed, corresponding to a relative error of 2.8% with respect to the system dimensions. This result presents an improvement on positioning accuracy for fingerprint positioning VLP systems, which build their own transmitters.

Deep learning models are now used in multiple contexts, including safety critical applications. However, it has been proven that small adversarial alterations to the input can undermine the performances of the model, leading to unreliable results, while being hardly visible to a human observer. Image watermarking share similarities with this field: a small information is embedded inside the media, aiming at being not perceivable but robust. Many attacks have been developed to remove watermarks. In this paper, we evaluate the effectiveness of multiple image transformations to remove adversarial perturbations from images. Our experiments on the MNIST dataset for a Projected Gradient Descent-based adversary demonstrate that many transformations can result in a significant gain in accuracy when classifying adversarial examples, while not degrading the quality of the images when the adversary is not present/non significant.

Light Field imaging systems allow capturing a larger amount of information of a scene with respect to conventional cameras. In fact, light field cameras record, in a single shot, multiple instances of the same scene taken from slightly different points of view. This enables new applications, like digital refocusing and free-viewpoint content creation. The main drawback of this hardware is in the trade-off between spatial and angular resolution, which has an impact on the depth of field of the refocused images. In this paper we propose a technique for increasing the depth of field by means of mul-tifocus fusion and, at the same time, for enhancing the quality of the content. The available views are processed in the Laguerre Gauss transform domain to perform a selective enhancement that sharpens the selected depth layer and blurs the others. The performed experiments prove the effectiveness of the proposed approach.

Light field imaging is increasingly spreading and its applications are reaching a larger number of users. The quality of the rendered content as perceived by the viewers is a key factor for the future development of this technology. Many factors can cause a degradation of the Light Field quality, from data acquisition/creation to the final rendering. To increase the perceived quality of the Light Field content, as usually performed for 2D and 3D content, processing techniques can be applied. In this work, an approach for enhancing Light Field videos is presented. The proposed approach is based on the characteristics of the human visual system and relies on the estimation of depth, motion and saliency. Those features guide a selective enhancement that is implemented in the Laguerre-Gauss Transform domain. The performed tests show the effectiveness of the proposed method.

In this work, the problem of video orchestration performed by combining information extracted by multiple video sequences is considered. The novelty of the proposed approach relies on the use of aesthetic features and of cinematographic composition rules for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues of aesthetic feature extraction from videos and video orchestration, in this work we exploit a set of features of a scene for automatically selecting the shots being characterized by the best aesthetic score. In order to evaluate the effectiveness of the proposed method, a preliminary subjective experiment has been carried out with experts from the audiovisual field. The achieved results are encouraging and show that there is space for improving the performances.

GNSS positioning with high Integrity and accuracy attributes is a key application for implementing more efficient and safer automated train systems. The goal is an overall system concept and train equipment that is competent in high multipath environments, suitable for being integrated into the train safe platform ensuring at the same time economy on total cost of operations. Connected cars applications rely also on accurate and safe positioning and have in common with the trains the operational environment and a centralized command and control system. This paper introduce the give and take paradigm to liaise train control systems and autonomous cars stake holders and develop a common GNSS platform. The ambition is to export to the autonomous cars the safety capabilities of train control systems demonstrated during tens of years of operations, and to exploiting the large car’s market potential to lower the GNSS platform costs. The paper starts with the characteristics of the European Railways Train Management Systems (ERTMS) that guarantees the highest safety levels through a platform which is interoperable, standard and certifiable with harmonized procedures. Then we describe the plan to validate and certify the GNSS positioning applications into the ERTMS, which is one of the Game Changer technologies of the ERTMS evolution plan. The synergy with the connected cars is discussed by first introducing the virtual track concept to drive a car on the road as a train on the railways and then by developing the virtual horizon to safely monitor the way ahead detecting obstacles. The roadmap is driven by the ERSAT GGC (Galileo Game Changer) H2020 project, involving the rail community, and the EMERGE initiative, dealing with the automotive sector.

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.

Industrial and Automation Control systems traditionally achieved security thanks to the use of proprietary protocols and isolation from the telecommunication networks. Nowadays, the advent of the Industrial Internet of Things poses new security challenges. In this paper, we first highlight the main security challenges that advocate for new risk assessment and security strategies. To this end, we propose a security framework and advanced tools to properly manage vulnerabilities, and to timely react to the threats. The proposed architecture fills the gap between computer science and control theoretic approaches. The physical layers connected to Industrial Control Systems are prone to disrupt when facing cyber-attacks. Considering the modules of the proposed architecture, we focus on the development of a practical framework to compare information about physical faults and cyber-attacks. This strategy is implemented in the ATENA architecture that has been designed as an innovative solution for the protection of critical assets.

Autonomous driving technology requires high accuracy and high integrity position and navigation. No individual technology can currently meet these requirements anywhere, anytime and under any condition. Sensor fusion is considered as the go-to-solution for the development of fully autonomous driving technology. GNSS is an element of a sensor fusion based navigation system that includes lidars/radars, inertial sensors and cameras. This work shows as the comparison between the speed’s vehicle calculated by odometer and one estimated by GNSS receiver installed on board of train through Doppler is an effective means to face multipath in railway environment, even when just one receiver is available. Moreover, the georeferenced knowledge of the railway is not essential when Doppler are compared, while the pseudorange comparison requires a guess of the baseline between the receivers. Moreover, the proposed multipath detection and exclusion method is fully compatible with other techniques for multipath mitigation. The outcomes of this work, undertaken for railway scenarios, can be partly exploited in driver-less car applications thanks to the analogies between these two transportation sectors.

International audience; Mission critical services are beginning to migrate to broadband from the trusted-but-limited existing systems. They need assured availability and confidentiality for communication in areas affected by a disaster, flash crowding or any catastrophic network failure. This paper describes a vision for an automotive virtual edge Communication scheme that helps healing stricken networks, utilizing any available resources for hosting network functions on vehicles. Emergency and essential service organizations who rush to the area would `bring their own network', i.e. computing capacity and connectivity tools, aboard their service vehicles. These vehicles form a dynamic community that shares resources, information and services, using the combined processing capacity via virtualized core functions. To realize such a scheme, innovative features are required, such as cooperative hosting, opportunistic vehicular resource virtualization, context-based edge SDN traffic prioritization, and ad-hoc vehicular community management, including multi-entity authentication

The present work shows the results of a performance analysis for evaluating the impact of spoofing on GNSS Integrity for transport applications. Spoofing represents a potential risk to be managed for GNSS safety-related land mobile applications. To this aim a Virtual Test Bed has been generated. A rover receiver has been developed through an SDR, and relevant spoofed signal injected through a hardware simulator. A performance analysis software simulator performed relevant integrity study. Stanford plots have been generated for analyzing integrity performance to guarantee a THR=10-9/h in DGNSS mode.

The work deals with introduction of GNSS (Global Navigation Satellite System) technology into the railway sector. Particularly, since the requirements imposed by the railway environment are stringent, the use of an external mean for providing integrity monitoring and augmentation is highly recommended. In this paper, we show an algorithm based on GNSS code double differences to identify and exclude multiple satellite faults by using data acquired by multiple reference stations. The simulations results carried out in this framework are presented in terms of detection probability and false alarm rate.

Changing environmental conditions, the demographic trends and the consequent increase of mobility demand lead us to consider a new integrated mobility model that is able to guarantee efficient management of traffic flows, reduction of accidents and consumption, and improvements in driving and travel comfort. Accurate localization, vehicular connectivity and communication, as well as cybersecurity, will play a key role for development of future Cooperative and Intelligent Transportation Systems (C-ITS). This work presents research activities underway at the University of L' Aquila and the Radiolabs Consortium, concerning the study of techniques and technologies for secure communication and accurate geo-localization, the design and prototype implementation of management architectures and vehicle platforms, the development of dynamic and cooperative navigation services. These activities also aim to support future services in the ITS field, with a particular focus on mobility of commercial vehicles, both in normal and emergency operating conditions. Distinctive elements of this work are the identification and the services implementation of real use cases, supported by the new GALILEO satellite constellation, currently available communication technologies (V2X/4G), and the emerging 5G.

International audience; Nowadays, Internet of Things (IoT) coupled with cloud computing begins to take an important place in economic systems and in society daily life. It has got a large success in several application areas, ranging from smart city applications to smart grids. Despite the apparent success, one major challenge that should be addressed is the huge amount of data generated by the sensing devices. The transmission of these huge amount of data to the network may affect the energy consumption of sensing devices, and can also cause network congestion issues. To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on a hierarchical architecture with smart nodes, smart gateways and data centers. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. BIA is evaluated based on the data collected from the M3 sensors deployed in the FIT IoT-LAB platform and according to different scenarios. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.

GNSS is being gradually adopted for both navigation and control of many safety critical transportation system. This paper focuses on the important area of high integrity GNSS for railway applications which is critical for safe use of GNSS. While aviation has led the development of high-integrity GNSS applications, the Railway High Integrity Navigation Overlay System ( RHINOS) effort aims to apply GNSS to railways utilizing similar integrity methodologies. In particular, it seeks to provide the accuracy necessary to support the most critical railway operations while assuring a very low probability of Hazardously Misleading Information ( HMI). The RHINOS efforts are studying the most appropriate architectures ( e.g., combinations of GNSS augmentations) and developing an integrity methodology suitable for the architecture that is chosen. This paper describes a reference RHINOS architecture and examines its performance under nominal and faulted conditions. The performance analysis is conducted through simulation using the Matlab Algorithm Availability Simulation Tool ( MAAST). MAAST, which was developed for aviation integrity analyses, was modified to support Protection Level ( PL) calculations based on a proposed RHINOS reference architecture. It calculates PLs at representative locations throughout Europe for both nominal and faulted cases. The nominal case assumes that all GNSS range measurements are bounded by fault-free error models. Error models derived from accepted Satellite Based Augmentation System ( SBAS) and Ground Based Augmentation System ( GBAS) models are used. The main exception is multipath, which is known to be more severe for trains than for aircraft. The fault cases examined are those where either ionosphere gradients, satellite ( ephemeris or clock) errors, or multipath exceed the nominal models. The integrity monitoring should detect and exclude the fault, if it is sufficiently large or not detected it, in which case it will bound its effect. Finally, sensitivity analysis is conducted to provide insights for designing the system. Different multipath assumptions are tested and different levels of mitigation are examined what level of mitigation and monitoring should be targeted.

Fuzzy authentication allows authentication based on the fuzzy matching of two objects, for example based on the similarity of two strings in the Hamming metric, or on the similiarity of two sets in the set difference metric. Aim of this paper is to show other models and algorithms of secure fuzzy authentication, which can be performed using the rank metric. A few schemes are presented which can then be applied in different scenarios and applications., to appear in Cryptography and Physical Layer Security, Lecture Notes in Electrical Engineering, Springer

This paper deals with the performance analysis of indoor positioning systems over Orthogonal Frequency-Division Multiplexing (OFDM) like signals. The interest in Indoor positioning systems has exploded in the last few years thanks to the diffusion of smartphones and all those applications related to smart environments. The study of solutions in that field is linked to the fact that inside the buildings the classic navigation systems, that rely on GNSS, cannot be used because of the absence or the weakness of the signals themselves. However, in those environments many services that rely on Radio Frequency (RF) signals are already present for communication purposes like Wi-Fi IEEE 802.11 and UMTS Long Term Evolution (LTE), both based on OFDMlike signals. Incidentally, we noticed that the bandwidth available is about 100 MHz, making this signals appealing also for positioning purposes. Moreover, both Wi-Fi and LTE in their standards have foreseen protocols for localization scopes. The main difference between these approaches is that LTE foresees the simultaneous Time Of Arrival (TOA) estimation on the signals received, while Wi-Fi foresees a sequential procedure performing a separate TOA estimation for each signal received. We refer to the interval between the TOA estimation among two consecutives beacons as Fine Timing Measurement (FTM) delay. In this contribution, we briefly review the market perspective and the technology at the state of the art to perform positioning services in indoor contest. A simulation tool that aims to represent a reference platform for the performance assessment and algorithms verification has been developed. In this work, the simulator tool is described in details and a comparison analysis among the positioning protocols is performed by means of Monte Carlo simulations. The main parameters under study are the FTM delay and the observation noise that is assumed to be white and Gaussian distributed. The estimation error is expressed directly in the user position domain while the target is moving in the space.

The adoption of the GNSS for the European Rail Train Management System – ERTMS is one of the objectives included into the ERTMS evolution plan to improve its competitiveness on the market. To this aim, in 2015 the European Global Navigation Satellite System Agency (GSA) approved the “ERTMS on SATELLITE –Enabling Application Validation (ERSAT–EAV)”, project with the object to verify the suitability of GNSS to generate Virtual Balises as an alternative/complement means to the physical balises prescribed by the ERTMS. The architecture of ERSAT EAV – based on joint use of multiple constellations and public augmentation networks - would be a contribution to foster cost-effective ERTMS solutions, primarily for the local and regional lines in Europe for which the economical sustainability is a priority. The paper describes the hazard’s mitigation techniques affecting the on board GNSS receiver when operating in harsh environments, and the scenarios for a smooth introduction of GNSS into the ERTMS system. Field test results carried out on a Test Bed implemented on a 50km line in Sardinia are presented and discussed.

Smart city services are enabled by a massive use of Internet of Things (IoT) technologies. The huge amount of sensors, and terminals with a great variety of typologies and applications, requires a secure way to manage them. Capillary networks can be seen as a short range extension of conventional access network in order to efficiently capture the IoT traffic, and are enablers for smart city services. They can include both IP and non-IP devices, and security can become an issue, especially when simple unidirectional communication devices are considered. The main goal of this paper is to analyze security aspects in IoT capillary networks including unidirectional and bidirectional IP or non-IP devices. We propose an algorithm for secure access for uni- and bi-directional devices. The security procedure is based on a secure key renewal (without any exchange in air), considering a local clock time and a time interval of key validity. Following previous work in 2014 by Giuliano et al. , in this paper we assess the duration of the validity of the time window, and present extended simulation results in terms of (average) transmission time in a realistic scenario, i.e., including the presence of disturber(s), then providing indications for the setting of the duration of the key validity time window. Finally, we present the benchmark analysis in order to assess the effectiveness of our approach with respect to other existing standards, as well as the security analysis in terms of typical attacks.

International audience; Nowadays, Internet of Things (IoT) coupled with cloud computing begins to take an important place in economic systems and in society daily life. It has got a large success in several application areas, ranging from smart city applications to smart grids. One major challenge that should be addressed is the huge amount of data generated by the sensing devices, which make the control of sending useless data very important.To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on a hierarchical architecture with simple nodes, smart gateways and data centers. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. BIA is evaluated based on the data collected from real sensors and according to different scenarios. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.

In this chapter, Bussgang blind deconvolution techniques are reviewed in the general Bayesian framework of minimum mean square error (MMSE) estimation, and some recent activities of the authors on both single-channel and multichannel blind image deconvolution, under the general framework of Bussgang deconvolution, are described. Applications of the single-channel Bussgang blind deconvolution approach to perform unsupervised texture synthesis is detailed. Moreover, the Bussgang blind deconvolution method is generalized to the multichannel case with application to image deblurring problems. Speci?cally, we address the restoration problem of poorly spatially correlated images as well as strongly correlated (natural) images and experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are given. A theoretical analysis to show the local convergence properties of the Bussgang deconvolution algorithm is also reported.

In this work, the problem of dynamic video scene creation obtained by combining information extracted from multiple video sequences is considered. The main novelty of the proposed approach relies on the use of aesthetic features for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues of aesthetic feature extraction from videos and video orchestration, in this work we exploit selected features of a scene for automatically selecting the shots being characterized by the best aesthetic score. In order to evaluate the effectiveness of the proposed method, a subjective experiment has been carried out with experts from the audiovisual field. The achieved results are encouraging and show that there is space for improving the performances.

The evolution plan of the European Railways Train Management System (ERTMS) includes the GNSS localization as one of the Game Changer technologies to improve the competitiveness of the ERTMS. GNSS will allow the implementation of cost-effective solutions to reduce the maintenance and operational cost without reducing the safety levels required by railway operations. The inherent low power of satellite navigation signals exposes GNSS-based solution to Radio Frequency threats, namely intentional or unintentional interference, that can lead to performance degradation or denial of service, and to spoofing/meaconing attacks, that can lead to receiver deception and hence to misleading PVT (Position, Velocity and Time) estimation. The aim of this paper is to present an architecture for detection and mitigation of radio-frequency hazards in a rail operational environment. The investigated solution is based on a Digital Beamforming Platform (DBP) coupled with advanced GNSS signal processing techniques for high rejection of GNSS interfering and counterfeit signals. This approach fully exploits the characteristics of the railway context, to support the evolution of the Location Determination System (LDS) based on GNSS in ERTMS Train Control System (TCS). This paper presents the DBP architectural design, focusing on the most meaningful and innovative solution foreseen for the prototype implementation. Each subsystem of the DBP is described in details, and a preliminary assessment of the performances is provided, by means of simulative and analytic tools.

The need for effective and reliable surveillance techniques is getting nowadays more and more of primary importance, especially in the actual scenario in which safety and security have become a priority. While classical techniques rely on video-based surveillance systems, such as Close-Circuit television, many studies show that also the audio signal can be effectively used for these purposes. There are many characteristics that make the audio signal particularly suited for this task and, above all, the fact that the analysis of the audio signal can greatly improve thanks to the introduction of automatic classification. Recently, a large focus has been on the use of Deep Neural Networks for classifying audio data and, in this work, we aim to test their performance in the audio surveillance field. In this contribution we propose an algorithm for audio events detection in noisy environments based on the use of deep recurrent neural network. The achieved results show satisfactory and improved performances with respect to state-of-the-art techniques.

The aim of this work is related to railway sector and particularly with the introduction of the GNSS (Global Navigation Satellite System) into that environment. More in details, our aim is to evaluate system performance in terms of integrity of the navigation data. In the train control system, the integrity is one of the most challenge feature to achieve. Under that assumption, an algorithm that is able to exclude multiple SIS (Signal in Space) faults has been designed. In the design of the approach a multi-constellation framework has been taken into account. Simulation results highlighted promising outcomes both in terms of detection probability and in terms of false alarm rate.

At the start of missions trains compliant with the European ERTMS/ETCS standard have to run in Staff Responsible Mode (SRM) until the track where the train is lying on is identified by the Control Center (i.e., the Radio Block Center). Since SRM implies very low average speeds, this procedure produces a waste of time, and then money, every time a service starts. Thus, rail infrastructure managers are starting to ask for GNSS based solutions, able to determine which is the track occupied by the train in a very short time. Thus, in this paper we investigate PVT solutions based on multiconstellation receivers and dedicated augmentation networks that allow to determine the unknown track at the start of mission with the required safety Integrity level (SIL-4). Particularly we focus our attention on RTK solutions that benefit of the constraint represented by the track for the train location, in order to speed up convergence. Considering that the saved money strictly depends on the time needed to solve phase ambiguities, a double frequency solution has been considered. In this contribution a detailed description of the overall processing and achievable performance is given. Assessment of the performance is provided by means of Monte Carlo simulations making use of observations recorded in the framework of the European Union Horizon 2020 Galileo-2014-1 ERSAT EAV Project. Index Terms— Railway applications, PVT estimation, Protection Level, Hazardous Misleading Information

International audience; —Cloud computing and Internet of Things (IoT) represent two different technologies that are massively being adopted in our daily life, playing a fundamental role in the future Internet. One important challenge that need to be handled is the enormous amount of data generated by sensing devices, that make the control of sending useless data very important. In order to face with this challenge, there is a increasing interest about predictive approaches to avoid to send high spatio-temporal correlated data. Belief Propagation (BP) algorithm is a method of performing approximate inference on arbitrary graphical models that is becoming increasingly popular in the context of IoT. By exploiting BP, we can derive effective methods to drastically reduce the number of transmitted messages, while keeping high the data throughput in the global information system. In this paper, we propose a BP approach in a hierarchical architecture with simple nodes, gateways and data centers. We evaluate the error bounding and propose a corrective mechanism to keep a certain quality of the global information in the architecture considered.