Railway High Integrity Navigation Overlay System Project Overview and Main Achievements. Alessandro NERI

Transcription

1 R H I N O S Railway High Integrity Navigation Overlay System Project Overview and Main Achievements Alessandro NERI

2 GSA H2020 RHINOS Project Railway High Integrity Navigation Overlay System RadioLabs Ansaldo STS SOGEI Nottingham University DLR Deutsches Zentrum Fuer Luft - Und Raumfahrt EV Univerzita Pardubice Stanford University Based on international cooperation between EU and USA Objective a positive step beyond the proliferation of GNSS platforms, mainly tailored for regional applications, in favour of a global solution. Work programme investigation of candidate concepts for the provision of the high integrity needed to protect the detected position of the train, as required by the train control system application. Reference Infrastructure GNSS (GPS and GALILEO) + SBAS (EGNOS and WAAS) + Local augmentation elements, ARAIM techniques and other sensors on the train are the add-on specific assets for mitigating the hazards due to the environmental effects which dominates the rail application. Ambition Fast release of the potential benefits of the EGNSS in the fast growing train signalling market.

3 ERTMS/ETCS Train Localization GPS RBC ETCS trainborne position report BALISE In ERTMS/ETCS Train location is determined by means of BALISES and ODOMETRY The Balises are transponders deployed at georeferenced points The odometer provides the relative positioning w.r.t. the last balise When the Balise Reader energizes a balise, it receives a message with the balise Id The on board computer (EVC) sends a POSITION REPORT to the Radio Block Center

4 The Virtual Balise Concept GPS BEIDOU RBC ETCS trainborne position report Interlocking Virtual Balise The GNSS based VIRTUAL BALISE READER generates the same information produced by a Balise Reader detecting a physical Balise, through the same logical and physical interface, then emulating the Balise reader behavior with respect to the train equipment. In this way the On Board ERTMS/ETCS location determination functions do not need to be changed.

5 Railway User Requirements Translation of Railway RAMS (Reliability, Availability, Maintainability, and Safety) requirements into GNSS integrity requirements Comparison of aviation and railway user requirements: Requirement Aviation Railway Integrity Risk MTBF Alert Limit/ Max. Confidence Interval IR=2x10-7 per 150 s THR= 4.8x10-6 per 1 hour CR=4x10-6 per 15 s MTBF=621 hours Tolerable Hazard Rate for VB detection: THR VB < 10-9 / hr (SOM) MTBF=3x10 5 hours 35 m (Vertical) Along track: 3 m Distance between parallel track axes: min m in stations, typically 6 m Availability

6 VBR Accuracy Requirements Supervised Location Req.: The train shall not trespass the Supervised Location without specific Moving Authority

7 VBR Accuracy Requirements Brake Activation Location Supervised Location Breaking Distance

8 VBR Accuracy Requirements VB Detection Limit Brake Activation Location Supervised Location Command, Control & Signaling Latency (in [km]) Breaking Distance

9 VBR Accuracy Requirements VB Location VB Detection Limit Brake Activation Location Supervised Location SIL-4 Train Location Confidence Interval Command, Control & Signaling Latency (in [km]) Breaking Distance Req.: To support INTEROPERABILITY Infrastructure Managers require that the same engineering rules are employed to deploy physical and virtual balises, In this way heterogeneous traffic consisting of trains equipped with physical BTM and trains equipped with Virtual BR can be handle by a a Radio Block Center, without modifications.

10 Railway Integrity Fault Tree ETCS core THR allocation to LDS safety functions 10

11 GNSS-based services for Train Control GNSS based train location determination can be considered a disruptive technology. It will succeed in replacing the current technologies based on balises and track circuits if and only if it will be COST-EFFECTIVE. CHALLENGE: SIL-4 COMPLIANCE Functionality Current EU Technology (ERTMS) SIS Integrity Monitoring Augmetation Accuracy Train Location Determination Single track Based on Balise X X Medium Train Location Determination Multiple tracks Train Integrity Based on Balise, Track Circuit Track Circuit + On Board Circuitry X X Medium, High X N High

12 Overlay Architecture Selection of candidate solutions concerning both augmentation and integrity monitoring infrastructures, and On Board Units starts from the mitigation actions related to the hazards identified during the Hazard Analysis Mitigation Actions Hazard Analysis Candidate Solutions Hazards Clock runoffs Ephemeris Faults Ionospheric storms Signal Distortions Constellation Rotations Multipath Jamming, Spoofing Mitigations SBAS & LADGNSS SBAS & LADGNSS LADGNSS, Multifrequency SBAS & LADGNSS SBAS & LADGNSS Train Autonomous Integrity Monitoring DBF + High Resilience DSP Train Autonomous Integrity Monitoring

13 RHINOS MAIN ACHIEVEMENTS 1. Railway Environment MODELLING 2. Definition of candidate solutions Integrity Monitoring and Augmentation System Reference Architecture On Board System Reference Architecture Local hazards detection and effects mitigation, Global Hazards Monitoring GNSS SISs Train ARAIM Confidence Interval Local Hazards Monitoring

14 RHINOS MAIN ACHIEVEMENTS 3. Cost and Benefits trade-off and selection of the reference architecture among the candidate solutions. GNSS based LDS Integrity Model (appropriate analytical methods and tools) cost and benefits, SWOT (Strength, Weakness, Opportunities, and Threats) 4. Realization of a Proof of Concept 5. Verification of the reference architecture performance 6. Dissemination and consensus sharing. RHINOS team members are part of the EU-U.S. Cooperation on Satellite Navigation Working Group-C on Next Generation GNSS

15 Track Discrimination Scenario Satellite Visibility Multipath Interference Risk Train Dynamics Railway station Reduced High High Null Along the line Good Low Low High (HS) ENABLING TECHNOLOGIES MULTICONSTELLATION GPS+GALILEO+ ГЛОНАСС+BEIDOU SBAS EGNOS, WAAS, SDCM Integrity Monitoring and Augmentation Services High Accuracy RTK, PPP (?) Multifrequency Rx E1 E5a E5b, L1 L2C L1C ARAIM

16 RTK: state of the art Dual Frequency TTFF (seconds) vs. baseline length Source: Feng et al. GPS RTK Performance Characteristics and Analysis 100% Availability 100% 35 km Reliability 60% 95% 35 km 35 km RTK Availability and Reliability may be critical, due to their fast decay with respect to baseline length

17 Track Discrimination When the railway consists of multiple tracks, the single track PVT estimate is combined with the track detection. We assume that the train can be located along one of M tracks and we denote with H k the hypothesis corresponding to the k-th track. The Bayesian (optimal) track detection rule selects the hypothesis corresponding the largest generalized likelihood ratio For each track we estimate the train mileage under the hypothesis that H k is true; We then use these mileage estimates in a likelihood ratio test, as if they were correct

18 RTK with Track Constraint Baseline expansion in Taylor s w.r.t. s b ; bˆ Then we have Measured double difference ( 1) ( ) ˆ m m s s s i b s ( m1 ) ( m) ssˆ ( m1) s ˆ ( m 1) ( m 1) tr op i o n i c c i L Hb DD β τ τ ˆ, 1,2 ( m1) ( m) HG s Ni ni i G ( m) X s ( j ) T H S ems Initial phase Ambiguity Train ss ( ) ˆ m S RS b q ˆ ( m 1) b I ( ) j 1 j 1 0 j 1 0 s 1 I Sat b s ss p s ˆ ( m 1) ( 1) ˆ m N j N j Sat s ( m)

19 RTK with Track constraint Dual frequency linearized system ˆ ( m1) P ( m1) P1 Hb DD β 1 HG 0 0 ( m) ˆ s ( m1) P ( m1) P2 Hb DD β2 0 0 HG ( 1) ( m 1) 1 ˆ m N L Hb DD β HG I ( m1) 2 2 ˆ ( m1) 2 0 N L Hb DD β HG I R Generalized Likelihood Ratio n P 1 P n2 1n1 2n2 Since we have undesired unknown parameters (the phase ambiguities) we have pdd/ H ( R / H ) k k k ( R) w( R) N 1, N2 p ( R / H, N, N ) P( N, N / H ) DD/ H, N k k w( R) k

20 RTK with Track constraint Dual frequency linearized system ˆ ( m1) P ( m1) P1 Hb DD β 1 HG 0 0 ( m) ˆ ˆ s ( m1) P ( m1) P2 Hb DD β2 0 0 HG ( 1) ( 1) 1 ˆ m m N L Hb DD β HG I ( m1) 2 2 ˆ ( m1) 2 0 N L Hb DD β HG I To reduce the search space we may employ the Wide Lane (WL) and Narrow Lane (NL) combinations P n1 P 2 n n n Combination Wide lane Narrow lane P WL L WL P NL L NL Weakness Too noisy Slow Ambiguity fixing

21 Track Constrained RTK Measurement equations P Hbˆ L DD β HG 0 sˆ n ( m1) ( m1) P ( m1) ( m) P NL NL NL ( 1) ( 1) ( 1) ˆ m m m WL Hb DD βwl HG W LI NWL WLn WL One advantage of the use of this pair is that an initial estimate of the phase ambiguities can be obtained by the Melbourne-Wübbena combination B L P MW WL NL Here the Melbourne-Wübbena combination is used to reduce the number of candidate ambiguities to be considered in the Generalized Likelihood Ratio computation.

22 A Posteriori Track Probability For each candidate phase ambiguity vector the estimated train mileage is H bˆ DD β k k P ( m) PNL 0 Hk Hk H ˆ ˆ k NL s K Hk, WL / H WL h ˆ WL N N L WLI H H bh DDH β k WL T 1 The A posteriori Probability of each tack is ( m ) ( m 1) T 1 1 ( m 1) ( m 1) T T 1 1 H P L H H P L K G H R R H G G H R R k NL WL k k NL WL 2 1 ˆ exp ν P( ) sˆ, WL 2 H, N N WL k N N WL 1 WL WL Prob H Rν k. 2 1 exp ˆ ν P( ) sˆ, WL 2 H, N NWL m k N N WL 1 WL Rν WL I ˆ ( sˆ ) ( sˆ ) 0 ν R Hb DD β N P sˆ,,, H, WL H NL k WL H WL N k WL k N N N WL I WLI

23 Test bed description Parallel tracks: 2 interaxis: 3 m Train on Track #1 Tracking Channels Measurements Quality Fixed Amb. RTK positioning accuracy Antenna Measurements update rate 120 channels GPS: L2, L2P, L2C, L5 GLONASS: L1 C/A, L2P, L2C Galileo: E1, E5a, E5b, E5a+b SBAS: WAAS, EGNOS, GAGAN, MSAS Very low noise GNSS carrier phase measurements (RMS< 0.2 mm) 10 mm + 1 ppm (horizontal)/10 mm + 1ppm (vertical) Standard Dorne Margoline with Choke Ring Antenna 50 Hz

24 A posteriori track probability (Results) Memoryless Single Epoch Detection Mileage Track Posterior Probability

25 A posteriori track probability (Results) Memoryless Single Epoch Detection Geometry Free Residual MSQE

26 Track Discrimination Performance Track Error Probability Single Epoch, M equispaced Tracks 1 Γe h Pe 1 erfc d M 2 2 N 0 epochs, M equispaced Tracks (Slow motion) beˆ ( j) Γ C I H G K S E h ν H H H H h h h k ( N, ) 1 O I Γe h Pe 1 erfc NOb M 2 2 DGNSS - N 0 epochs, M equispaced, Rank Order Statistics Detector N P P P O N0 ( NO, II ) O h NOh e 1 1 e e hk h

27 Conclusions As confirmed by the experimental activity, the coarse estimate of the train location provided by the Wide Lane combination is good enough to reliably discriminate the track. The main advantage is the TRADE-OFF between ACCURACY and TIME needed to discriminate the track. In fact, in our case, we do not have to wait for ambiguity fixing. To achieve track error probabilities compatible with SIL 4 operational requirements even in strong multipath environment, TEMPORAL INTEGRATION and MULTIPLE CONSTELLATIONS can be applied. Nevertheless, effectiveness of temporal integration can be impaired by MULTIPATH errors highly correlated in time. To reduce this effect, proceeding at the maximum authorized speed when in Start of Mission mode is recommended.

28 THANKS FOR YOUR ATTENTION

29 SIS Fault The Hazard essentially reduces to the ephemeris errors and Tropospheric errors Effect modeled as a satellite position error b on the i-th satellite Let Single difference error

30 SIS Fault Let b b cos p sin B B B q B q b B p i,, i i,q i, m p m i b B b RIM p B b e erimq SD( b) 2 i i B rtrain rtrain

31 Project Objectives Objective 1: To DEFINE THE ARCHITECTURE of a train Location Detection System (LDS) and of the supporting infrastructure, with the following properties joint use of GPS and GALILEO and wide area integration monitoring and augmentation networks (WAAS, EGNOS) standard interface for providing Safety of Life services for railways through SBASs, regional augmentations or hybrid SBAS/GBAS systems; compliance with European and US railway requirements and regulations; sharing as much as possible of the supporting infrastructure and on board processing, including new developments such as ARAIM, with the avionics (and automotive) field, provisioning of a set of functionalities tailored to the specific needs of the rail sector.

32 Mean on double differences [m] Project Objectives Objective 2: To assess the performance of the defined architecture by means of: a PROOF-OF-CONCEPT integrating, in a virtualized testbed, real railway environment data sets, rare GPS SIS faults simulated faults for the new constellations; ANALYTICAL METHODS for verification and safety evidence of defined architecture according to railway safety standards (e.g. CENELEC EN 50129, etc.) Mean values on double differences for all the satellites in view GPS time [s] x 10 5

33 Project Objectives Objective 3: To contribute to the missing standard in the railway sector about the way of integration of GNSS-based LDS, into current Train Control System standards (e.g. ERTMS) by publishing a comprehensive GUIDE on how to employ, in a costeffective manner, GNSS, SBAS and other local infrastructures in safety related rail applications worldwide, by defining a STRATEGIC ROADMAP for the adoption of an international standard based on the same guide.