RH INOS “Railway High Integrity Navigation Overlay System”

Project Overview and Main Achievements

Alessandro NERI

GSA H2020 RHINOS Project

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.

Railway High Integrity Navigation Overlay System

RadioLabs

Ansaldo STS

SOGEI

Nottingham University

DLR Deutsches Zentrum Fuer Luft - Und Raumfahrt EV

Univerzita Pardubice

Stanford University

ERTMS/ETCS Train Localization

GPS

RBC

BALISE

position report

ETCS

trainborne

• 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

The Virtual Balise Concept

• 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.

GPS BEIDOU

RBC

Virtual Balise

position report

ETCS

trainborne Interlocking

• In this way the On Board ERTMS/ETCS location determination

functions do not need to be changed.

Railway User Requirements

Requirement Aviation Railway

Integrity Risk IR=2x10-7 per 150 s THR= 4.8x10-6 per 1 hour

Tolerable Hazard Rate for VB detection: THRVB < 10-9/ hr (SOM)

MTBF CR=4x10-6 per 15 s MTBF=621 hours

MTBF=3x105 hours

Alert Limit/ Max. Confidence Interval

35 m (Vertical) • Along track: 3 m • Distance between parallel track

axes: min. 4.75 m in stations, typically 6 m

Availability 0.99 – 0.99999 0.9999854

5

• Translation of Railway RAMS (Reliability, Availability, Maintainability, and Safety) requirements into GNSS integrity requirements

• Comparison of aviation and railway user requirements:

VBR Accuracy Requirements

Supervised Location

Req.: The train shall not trespass the Supervised Location without specific Moving Authority

VBR Accuracy Requirements

Supervised Location

Breaking Distance

Brake Activation Location

VBR Accuracy Requirements

Supervised Location

Breaking Distance Command, Control & Signaling Latency (in [km])

Brake Activation Location

VB Detection

Limit

VBR Accuracy Requirements

Supervised Location

Breaking Distance

Brake Activation Location

Command, Control & Signaling Latency (in [km])

SIL-4 Train Location Confidence Interval

VB Detection

Limit

VB Location

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.

Railway Integrity Fault Tree ETCS core THR allocation

to LDS safety functions

10

GNSS-based services for Train Control

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

Based on Balise, Track Circuit

X X Medium, High

Train Integrity Track Circuit + On Board Circuitry

X N High

• 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

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

Hazards Mitigations

Clock runoffs SBAS & LADGNSS

Ephemeris Faults SBAS & LADGNSS

Ionospheric storms LADGNSS, Multifrequency

Signal Distortions SBAS & LADGNSS

Constellation Rotations SBAS & LADGNSS

Multipath Train Autonomous Integrity Monitoring

Jamming, Spoofing DBF + High Resilience DSP Train Autonomous Integrity Monitoring

Hazard Analysis

Mitigation Actions

Candidate Solutions

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,

Train

ARAIM

Confidence

Interval

Global Hazards

Monitoring

Local Hazards

Monitoring

GNSS SISs

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

Track Discrimination

ENABLING TECHNOLOGIES

Scenario Satellite Visibility

Multipath Interference Risk

Train Dynamics

Railway station

Reduced High High Null

Along the line

Good Low Low High (HS)

• 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

RTK: state of the art

RTK Availability and Reliability may be critical, due to their fast decay with

respect to baseline length

Source: Feng et al. “GPS RTK Performance Characteristics and Analysis”

100%

95%

100% Reliability

60%

Availability

35 km

35 km

35 km

TTFF (seconds) vs.

baseline length

Dual Frequency

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 Hk the hypothesis corresponding to the k-th track.

The Bayesian (optimal) track detection rule selects the hypothesis

corresponding the largest generalized likelihood ratio

Track Discrimination

• For each track we estimate the train mileage under the hypothesis that Hk is true;

• We then use these mileage estimates in a likelihood ratio test, as if they were correct

Measured

double

difference

RTK with Track Constraint

q

p

RS

Baseline expansion in Taylor’s w.r.t. s

Then we have

( 1)

( 1 ( )

ˆ

)ˆm

m

s s

m ss

bb

b;

( 1) ( )ˆ m ms s s

( 1)ˆ mb

b

Initial phase

Ambiguity

( 1) ( 1)ˆ m m tr o

ii

op i n

ic c Hb DD β τ τL

( 1) ( )ˆ , 1,2ii

mm s i N nGH

( )

( )

ˆ m

Trainm

s ss

XG

( )j T

MSH S e

1 1( )0

0Sat Sat

j jj

N j N j

I 1S

1 I

( 1)

( )

ˆ m

m

s s

ss

b

( 1)ˆ ms ( 1)ˆ ms

s

RTK with Track constraint

( 1)

1 1

( 1)

2 2

( 1)1

( 1)

( 1)

( 1)

( 1

1

(

1

1)2 2

)

2

(

)

ˆ

ˆ

0 0

0 0

0

0

ˆ

ˆ

m

m

m

m P

m P

m

mm

ms

HbP DD β

P Hb DD β

L DD β

HG

HG

HG I

HG

Hb

L DDN

βHb

N

I

1

2

1 1

2 2

P

P

n

n

n

n

Generalized Likelihood Ratio

Dual frequency linearized system

/ ( / )( )

( )

kH k

k

p H

w

DD RR

R

1 2

1 2 1 2

,/ ,

( / , ) ( / )

(

, ,

)

kk kH

p H P H

w

N N

DD NR N N N

R

N

R

Since we have undesired unknown parameters (the phase ambiguities) we have

RTK with Track constraint

( 1)

( 1)

( 1)

( )

1

1 1

( 1)

2 2

( 1)1 1

( 1)2 2

( 1)

) 2( 1

ˆ

ˆ

0 0

0 0

0

0ˆ

ˆ

ˆ

m

m P

m P

m

m

m

m

m

m

s

HbP DD β

P Hb DD β

L DD β

HG

HG

HG I

HG

Hb

L DDN

βHb

N

I

1

2

1 1

2 2

P

P

n

n

n

n

To reduce the search space we may employ the Wide Lane (WL) and

Narrow Lane (NL) combinations

Dual frequency linearized system

Combination Weakness

Wide lane Too noisy

Narrow lane

Slow Ambiguity fixing

WLL

WLP

NLP

NLL

Track Constrained RTK

Measurement equations

( 1)

( 1

( 1) ( 1)

( 1) (

(

1

)

))

ˆˆ

ˆ

m

WL

m

NL

mW

m P m P

NL NL

m m

WL WL L WL WL

s

N

DD β HG 0 n

DD β HG I n

P Hb

L Hb

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

MW WL NL B L P

Here the Melbourne-Wübbena combination is used to reduce the number of candidate ambiguities to be considered in the Generalized Likelihood Ratio computation.

A Posteriori Track Probability

For each candidate phase ambiguity vector the estimated train mileage is

( )

, /

ˆˆ

ˆˆ k k k

k hWL

kk k

WL

PH H H NLNLm

H HWL WL H WLH H

s

N

H b DD βP 0K

L I DD βHN

b

1

( ) ( 1) 1 1 ( 1) ( 1) 1 1

k NL WL k k NL WL

T Tm m T m m T

H P L H H P L

K G H R R H G G H R R

, 1

, 1

2

ˆ ,

2

ˆ ,

1ˆexp ( )

2Prob .

1ˆexp ( )

2

WLHk WL

WL WL

WLHk WL

WL WL

WLs

k

WLsm

P

H

P

N

ν

N

ν

NN R

NN R

ν N

ν N

The A posteriori Probability of each tack is

,

ˆ , , ,ˆ ˆ ˆ( ) ( )

k kWL WLHkW

WLL

P

NLs H H L

WL

Ws s

N

N NN

0Iν R Hb DD β

IIN

Test bed description

Parallel tracks: 2

interaxis: 3 m

Train on Track #1

Tracking

Channels

120 channels

GPS: L2, L2P, L2C, L5

GLONASS: L1 C/A, L2P, L2C

Galileo: E1, E5a, E5b, E5a+b

SBAS: WAAS, EGNOS,

GAGAN, MSAS

Measurements

Quality

Very low noise GNSS carrier

phase measurements (RMS<

0.2 mm)

Fixed Amb. RTK

positioning

accuracy

10 mm + 1 ppm (horizontal)/10

mm + 1ppm (vertical)

Antenna Standard Dorne Margoline with

Choke Ring Antenna

Measurements

update rate

50 Hz

A posteriori track probability (Results)

Memoryless Single Epoch Detection

Mileage

Track Posterior Probability

A posteriori track probability (Results)

Memoryless Single Epoch Detection

Geometry Free

Residual MSQE

Track Error Probability

• Single Epoch, M equispaced Tracks

• N0 epochs, M equispaced Tracks (Slow motion)

• DGNSS - N0 epochs, M equispaced , Rank Order Statistics Detector

( , ) 11

2 2

O hN I

e OP erfc N bM

Γ e

0

( , )1 1O O

O

NhON II N h

e e e

h k

NP P P

h

ˆb e

Track Discrimination Performance

( )

h h h k

j

h H H H H νΓ C I H G K S E

11

2 2

h

eP erfc dM

Γ e

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.

THANKS FOR YOUR ATTENTION

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

SIS Fault

• Let

cos sinp q

b

B b B B

Bp

Bq b

12 2 2

, ,q, ,

( ) 2i iRIM RIMp q

i i

m p mi

SD i i

Train Train

b

B r r

e eB Bb b

b

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.

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.)

3.975 3.98 3.985 3.99 3.995 4 4.005

x 105

0

5

10

15

20

25

30

35

40

45

50Mean values on double differences for all the satellites in view

GPS time [s]

Mean o

n d

ouble

diffe

rences [

m]

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 cost-

effective 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.