Development of Algorithms for use in the Galileo Time Service Provider

D Baines1, J A Davis1, G Parkin1, P Harris1, A Batchelor2, J. M. Pieplu3, J. H. Hahn4, A Bauch5, V. Pettiti6, P. Uhrich7, R. Jones8, S. Bedrich9, T. Levin9, M. Stevens1

1National Physical Laboratory, Hampton Road, Teddington, Middlesex, UK2Thales Research and Technology, Worton Drive, Reading, UK

3European GNSS Supervisory Authority, Brussels, Belgium4Galileo Project Office, European Space Agency, ESTEC, Noordwijk, The Netherlands

5Physikalisch-Technische Bundesanstalt, Braunschweig,Germany6Istituto Nazionale di Ricerca Metrologica, Torino, Italy

7LNE-SYRTE, UMR CNRS 8630, Observatoire de Paris, Paris, France8Helios Technology Ltd,Aerospace Boulevard, Farnborough, UK

9Kayser-Threde GmbH, Munich, Germany

Friday, 23 May 2008

2

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

3

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

4

The Prototype TSP

Function• Act as the link between UTC and GST(MC) by providing daily

steering corrections to GST(MC).

Requirements• (UTC – GST(MC)) offset must not exceed 25 ns (1σ), 50 ns (2σ).• (UTC – GST(MC)) Offset uncertainty must not exceed 13 ns (1σ), 26

ns (2σ).• Normalised frequency offset (τ

= 1 day) must not exceed 5.5x10-14

(2σ).• Day to day difference in the frequency steer does not exceed 1x10-14.

• Requirements depend on the performance of both the PTF and TSP.

Friday, 23 May 2008

5

The Prototype TSP

Awarded to the Fidelity consortium:• Contains 4 core UTC(k) labs,

INRiM, NPL, OP and PTB.

Friday, 23 May 2008

6

The Prototype TSP

CONCEPT• UTC / TAI is computed up to 50 days in arrears by BIPM. • The TSP therefore constructs a free running ensemble

timescale CTSP and steered timescale CTSPS from measurements of all available high quality atomic clocks within the core UTC(k) labs.

• Concept includes the future inclusion of associate UTC(k) labs:– Most other European laboratories wishing to provide their

clock data to the TSP.– Need to be able to perform TWSTFT/CV with core

labs/PTF.– very inclusive solution.

• TSP does not perform time-transfer directly.

Friday, 23 May 2008

7

TSP Interfaces

GalileoPTF

UTC(k) LabUTC(k)

LabUTC(k) Lab

GalileoPTF

GPSRx

GPSRx

Galileo Rx

GalileoRx

TWSTFTTX/Rx

TWSTFTTX/Rx

Time Service Provider • Data pre-processing

• Data Archiving • TAI Prediction

BIPM

EGNOS

Loran-C

GalileoTimingUsers

GSS

Friday, 23 May 2008

8

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

9

The Algorithms

VerificationSegment

Control Segment

Verif

icat

ion

Segm

ent

Con

trol S

egm

ent

Main flow In/out DB

MMI Alarm

TSP GMS BIPM UTC(k)

XINT

DD

PPGPS

PPTW S

PPCLK

PPGAL

GMS BIPM WWWUTC(k)

XINT

MSG

TPREDSTEER

CCLK

PPCLK:Pre-process Clock Data•Detect anomalies.•Compute noise parameters &ADEV of clocks.•Provide CCLK with (UTC(k) – CI)& (GST(MC) – Cl)data.

PPTWSTFT:Pre-process TWSTFT Data•Detect anomalies.•Compute TT links.•Provide PPGPSwith processed TWSTFT data.•Provide CCLK with TT results.

PPGPS:Pre-process GPSData•Detect anomalies.•Compute TT links.•Validate (GPSCV -TWSTFT) differences.•Provide CCLK with TT results.

CCLK:Composite ClockAlgorithm•Compute free-running ensembletimescale CTSP from max of 100 clocks.•Deal with clock frequency steers.•Compute ADEV stabilities of CTSP .•Provide results toTPREDSTEER.

TPREDSTEER:Prediction and Steeringalgorithm•Compute internalsteered timescale CTSPSmonthly & predict (UTC-GST(MC)) for up to 50 days. •Daily estimation & prediction of (UTC-GST(MC)).•Compute daily steering parameters to steer GST(MC) to UTC.

Friday, 23 May 2008

10

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

11

Functionality of Pre-Processing elements• Parsing in of data.• Compute all possible clock differences (PPCLK) and

TT links (PPGPS & PPTWSTFT).• Run data through Kalman filter (same filter developed

for TPREDSTEER).• Run whiteness test on Kalman residuals, checking

correct noise parameter models. • Identify anomalies.• Test for bad clocks (PPCLK) and bad TT links

(PPGPS & PPTWSTFT).• Run n-cornered hat (PPCLK).• Compute noise parameters for individual clocks

(PPCLK) and TT links (PPGPS & PPTWSTFT).• Output data to CCLK.

Friday, 23 May 2008

12

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

13

PPCLK

• Example of real data run through PPCLK with examples of anomalies:– INRiM clock data from 16th Nov 2007 to 6th Dec

2007, MJD 54420 to MJD 54440. • Types of anomalies:

– Outliers, Noise parameter anomalies (Validating that the noise model used for each clock is physically realistic), diurnal instabilities, bad clocks.

Friday, 23 May 2008

14

PPCLK - inputs

5.442 5.4425 5.443 5.4435 5.444 5.4445

x 104

-2

-1

0

1

2

3

4

5

6

7

8x 10

-5 UTC - CI Raw Measurements

MJD

Clo

ck O

ffset

Clock 1Clock 2Clock 3Clock 4Clock 5Clock 6Clock 7

Time offsetanomaly

(H maser failing on MJD 54431)

Bad clock

Friday, 23 May 2008

15

PPCLK - outputs

5.442 5.4425 5.443 5.4435 5.444 5.4445

x 104

-2

-1

0

1

2

3

4

5

6

7

8x 10

-5 UTC - CI Pre-Processed Measurements

MJD

Clo

ck O

ffset

Clock 1Clock 2Clock 3Clock 4Clock 5Clock 6Clock 7

Bad clock identified and removed

Time offset identified and removed

Friday, 23 May 2008

16

PPCLK - inputs

5.442 5.4425 5.443 5.4435 5.444

x 104

24

24.2

24.4

24.6

24.8

25

25.2

25.4

25.6

25.8

26UTC - CI Raw measurements; Number of Good Points

MJD

Num

ber O

f Goo

d Po

ints

Clock 1Clock 2Clock 3Clock 4Clock 5Clock 6Clock 7

Friday, 23 May 2008

17

PPCLK - outputs

5.442 5.4425 5.443 5.4435 5.444

x 104

0

5

10

15

20

25UTC - CI Pre-Processed measurements; Number of Good Points

MJD

Num

ber O

f Goo

d Po

ints

Clock 1Clock 2Clock 3Clock 4Clock 5Clock 6Clock 7

Friday, 23 May 2008

18

PPCLK – detecting the anomalies, before

Time offset anomaly in Clock 6

5.442 5.4425 5.443 5.4435 5.444 5.4445

x 104

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

-5 Kalman Filter Residuals

MJD

Res

idua

l Offs

et

Residuals , Clock 3 (1350219) - Clock 6 (1351115)

Friday, 23 May 2008

19

PPCLK – detecting the anomalies, before

5.44265 5.4427 5.44275 5.4428-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

-5 Kalman Filter Residuals

MJD

Res

idua

l Offs

et

Residuals , Clock 3 (1350219) - Clock 6 (1351115)

Zooming in on anomaly detection

Friday, 23 May 2008

20

PPCLK – detecting the anomalies, after

5.442 5.4425 5.443 5.4435 5.444 5.4445

x 104

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

-9 Kalman Filter Residuals

MJD

Res

idua

l Offs

et

Residuals, Clock 3 (1350219) - Clock 6 (1351115)

Threshold set to approx 5σ, identifies anomaly as offset & is removed

Friday, 23 May 2008

21

PPCLK

103 104 105 10610-15

10-14

10-13

10-12ADEV estimates from n cornered hat, INRiM clocks

tau

Sigm

a

ADEV, Clock 1ADEV, Clock 2ADEV, Clock 3ADEV, Clock 4ADEV, Clock 5ADEV, Clock 6ADEV, Clock 7

•After only 20 days can distinguish between Cs & H masers.

•With several 100s days expect very good ADEVs.

•Only 10 days of data for Cl 1, H maser.

Friday, 23 May 2008

22

Validating the clock noise models

• Apply Kalman filters developed for the TPREDSTEER algorithm to each pair of clocks in turn.

• If the clock model is good then the Kalman filter residuals should be white within statistical uncertainty.(Simple whiteness test developed: 1 = white noise).

• Residual deviation obtained from real data should agree with that computed by the filter from the noise parameters (Variance ratio).

Friday, 23 May 2008

23

Validating the clock noise models

5.442 5.4425 5.443 5.4435 5.444

x 104

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4Daily residual Whiteness Tests

MJD

Tim

e O

ffset

Daily Whiteness Estimates, Cl 2 (1401102) - Cl 3 (1350219)

Friday, 23 May 2008

24

Validating the clock noise models

5.442 5.4425 5.443 5.4435 5.444

x 104

0.4

0.6

0.8

1

1.2

1.4

1.6Daily Residual Variance Ratio

MJD

Rat

io

Residual Variance Ratio, Cl 2 (1401102) - Cl 3 (1350219)

Friday, 23 May 2008

25

PPTWSTFT: TWSTFT PTB – IT link, MJD 54409-54545

103 104 105 10610-15

10-14

10-13ADEV, HDEV, MDEV Estimates Time Transfer Links

tau

AD

EV, H

DEV

, MD

EV

ADEVHDEVMDEV

Friday, 23 May 2008

26

PPGPS: OP – NPL link, MJD 54519-54545

102 103 104 10510-13

10-12

10-11ADEV, HDEV, MDEV Estimates Time Transfer Links

tau

AD

EV, H

DEV

, MD

EV

ADEVHDEVMDEV

Friday, 23 May 2008

27

Presentation

• Introduction: the Prototype TSP• The Algorithms• The Pre-Processing elements • Running real data through the algorithms and

detecting anomalies• Summary

Friday, 23 May 2008

28

Summary• TSP algorithms are currently being tested and

integrated at NPL.• New techniques for identifying anomalies have been

implemented into the TSP algorithms.• The pre-processing elements are doing a good job at

identifying anomalies from real clock and time transfer data.

• Operations due to start end Summer 2008.• Prototype TSP plans to be operational in early 2009.

Friday, 23 May 2008

29

Friday, 23 May 2008

30

EXPECTED PERFORMANCE OF THE TSP ENSEMBLE ALGORITHM

3 4 5 6 7 8Log10(τ) -16.00

-15.75

-15.5

-15.25

-15.00

-14.75

-14.5

-14.25

-14.00

-13.75

-13.5

-13.25

-13.00 Log10(ADEV)

Caesium

H Maser

Optimum ensemble

Log10 (τ)

Log 1

0(A

DEV

)