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  • Periodic Reporting for period 1 - RHINOS (RHINOS - Railway High Integrity Navigation Overlay System will define a GNSS-based system to support the localization of trains respecting the challenging requirements of the railway safety standards.)
H2020

RHINOS Report Summary

Project ID: 687399
Funded under: H2020-EU.2.1.6.

Periodic Reporting for period 1 - RHINOS (RHINOS - Railway High Integrity Navigation Overlay System will define a GNSS-based system to support the localization of trains respecting the challenging requirements of the railway safety standards.)

Reporting period: 2016-01-01 to 2016-09-30

Summary of the context and overall objectives of the project

Main objectives of RHINOS are: 1: To define the architecture of a train Location Detection System; 2: To assess the performance of the defined architecture by means of: a proof-of-concept integrating, in a virtualized testbed, rich sets of data collected in a real railway environment, historical time series related to rare GPS SIS fault events concerning both satellite malfunctions and atmosphere anomalous behaviours, including simulated faults for the new-coming GALILEO constellation; 3: To contribute to the missing standard in the railway sector about the way of integration of GNSS-based LDS, into current TCS standards (e.g. ERTMS).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the period covered by the report the following activities have been performed on WP1, WP2, WP3, WP4, WP5, WP6, WP7 and WP8.
The RHINOS project begun with the WP2 activities concerning the definition of the scenarios for the railway control systems – mainly European and U.S.A. – in order to provide a solid foundation and a common knowledge about the railway context and its specific needs.
In parallel, harmonization of railway and avionics concepts and terminology has been performed. The objective owas to describe links among GNSS Safety-of-Life service quality measures (Accuracy, Integrity, Continuity and Availability)] and quality attributes of railway signalling systems in terms of RAMS (Reliability, Availability, Maintainability and Safety) according to the CENELEC standards EN 50126 (RAMS), EN 50128, EN 50129, and also EN IEC 61508.
Excepting Integrity Risk other GNSS quality measures (continuity and availability) have been analysed because they are very related to IR and cannot be evaluated independently. Terminology for interpretation of GNSS quality measures in terms of railway RAMS has been described and GNSS quality measures by means of failure modes analysed. Further, the required GNSS Integrity Risk (IR) per operation by aviation has been translated into railway Hazard Rate per 1 hour. The work related to IR translation to HR from the augmentation system point of view will continue within the RHINOS project in next months, because it is critical to find a consensus on it between GNSS and railway specialists.
The results of the analysis of the impact of aviation GNSS Continuity Risk on reliability / availability of train LDS intended for railway safety-related systems including ERTMS/ETCS have been reported in D 3.2. Attention has been mainly focused on examination of the CR impact on the LDS architecture with reactive fail-safety and Travelling Virtual Balise concept.
Moreover, to meet and justify demanding ETCS safety (THR) requirements for GNSS using the existing EGNOS Safety-of-Life service, a novel Travelling Virtual Balise concept has been proposed.
WP4 has been devoted to the analysis of the state of the art. The Modernization of RAIM constituted by the ARAIM, that includes multiple constellations and two signalling frequencies, is considered important to RHINOS because it can detect and isolate faults that are local to the train. In contrast, SBAS and GBAS do not have strong visibility into multipath, spoofing, or radio frequency interference. Modifications to ARAIM to cope with local faults to be expected along the train track, including techniques to identify satellites that are likely in unobstructed view of the GNSS receiver on the train (i.e., NLOS propagation), and a modification to the ARAIM algorithm to detect and isolate satellite signals that suffer large errors due to multipath with either NLOS or LOS/NLOS propagation have been investigated.
The state of the art and future trends for two-tiers augmentation and integrity monitoring architectures and algorithms for SIS and two-tiers component failure detection and exclusion, integrity assessment, augmentation, and data computation in the railway environment has been analysed .
The current status of GNSS Augmentation Network services in terms of architectures, services, and standards, together with hints about the future trends and relevant updates suitable for meeting RHINOS target applications (railway, aviation, automotive) have been investigated, and some recommendations for the exploitation of high accuracy and high integrity systems for railway applications have been given.
Finally, the state of the art on the development of SBAS and GBAS augmentations to GNSS, their demonstrated performance in terms of safety, reliability, and availability, and their applications to civil aviation has been considered. Activities on Railway environment modelling (WP5) have been focused on those landscape features in the immediate vicinity of railway tracks that can have a deleterious effect on positioning derived from GNSS signals. Models of sky view and obscuration elevation masks relevant to obscuration and multipath effects on GNSS signals have been investigated. During the reporting period, activities on Local hazards' detection and effects quantification (WP6) have been started. They include the analysis of the local hazardous error contributions to the user equivalent range error in the railway environment, and the development of models for the nominal and anomalous ranging errors and investigations on GNSS aiding techniques to improve GNSS KPIs in the railway environment.
The RHINOS project has defined a set of candidate Overlay Reference Architectures (WP7). Thus, for each of them SWOT analysis have been performed. This led to the selection of a reduced set of candidate solutions. Finally, during the reporting period the activities related WP8 – RHINOS On Board Subsystem Reference architectures (WP8), have been mainly focused on the definition of the architecture for the On Board subsystem, as well as of the interfaces between the Integrity Monitoring and Augmentation subsystem and the On Board subsystem, and to the review of the analytical model for the system performance assessment.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

RHINOS is a safety critical navigation service for trains. As such, it will be based in part on similar systems developed for aviation. These include SBAS that place GNSS reference receivers at known location spread over continental areas.
ARAIM is needed for RHINOS because ARAIM mitigates the impact of hazards that are local to the train. These faults are not visible to SBAS reference stations that may be hundreds of kilometres away or even GBAS reference stations that may be several kilometres distant.
The Train Integrity Monitoring System must manage the errors that occur in the immediate vicinity of the locomotive. They include signal blockage, signal reflections, radio frequency interference, and spoofing.
The RHINOS project has defined a set of candidate Overlay Reference Architectures. Thus, after the compilation of the list of candidate solutions, for each of them a preliminary performance assessment and a SWOT analysis have been performed.
The following groups of Candidate Augmentation System have been chosen for the analysis: SBAS; GBAS; HA/DGNSS; NRTK plus CRAIM; 2-Tiers (SBAS+LADGNSS); NRTK+ARAIM; THA/RAIM; PPP/ARAIM/CRAIM.
In the next period, the RHINOS project will set up a “proof-of-concept”, in the form of a simulated demonstrator of the reference architecture. The demonstrator will be fed with real data provided by the ERSAT–EAV railway test bed located in Sardinia and by Stanford University MAAST simulator.
KPI will be defined and evaluated to verify the effectiveness of the reference architecture.

Related information

Record Number: 196340 / Last updated on: 2017-03-29
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