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.
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The Prototype TSP
Awarded to the Fidelity consortium:• Contains 4 core UTC(k) labs,
INRiM, NPL, OP and PTB.
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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)
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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
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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
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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.
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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).
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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)
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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)
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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
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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
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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
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Friday, 23 May 2008
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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
)