time transfer – algorithms for the galileo programme · time transfer – algorithms for the...
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Time Transfer – Algorithms for the Galileo Programme
John Davis 3rd June 2009
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Monday, 22 March 2010
2
WHY DO WE NEED A STABLE REFERENCE TIMESCALE FOR GALILEO?
• Galileo will be used for time and frequency dissemination, requires traceability to UTC
• Stable timescale required for synchronising clocks on satellites, and obtaining optimal performance from Galileo’s navigation services.
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Monday, 22 March 2010
3
REQUIREMENTS OF GALILEO REFERENCE TIMESCALE, GST(MC)
• Physical realisation of a timescale plus back- up GST(MC).
• TAI – GST(MC) < 50 ns (2σ)• TAI – GST(MC) uncertainty < 26 ns (2σ)• TAI – GST(MC) Normalised frequency
uncertainty < 5.4E-14 (2σ) (τ
= 1day) • GST(MC) normalised frequency stability <
4.5E-15 (τ
= 1day)
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Monday, 22 March 2010
4
COMPUTATION AND AVAILABILITY OF UTC
• Coordinated Universal Time computed once per month by BIPM
• Based on an ensemble of mainly active hydrogen masers and high performance caesium clocks
• National standards laboratories maintain UTC(k) physical realisations of UTC,
• UTC – UTC(k) offsets computed up to 50 days in arrears
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Monday, 22 March 2010
5
WHY DO WE NEED TIMESCALE ALGORITHMS?
• GST(MC) stability and reliability requirements cannot be achieved using a single clock
• Requirement to steer GST(MC) timescale to UTC / TAI
• Need to obtain best estimate of timescale offsets e.g. TAI – GST(MC) using satellite time transfer methods
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Monday, 22 March 2010
6
TIME AND FREQUENCY RESPONSIBILITY WITHIN THE GALILEO REFERENCE
TIMESCALE
• Precise Time Facility (PTF) provides a stable physical timescale for navigation applications.
• Time Service Provider (TSP) provides the traceablilty to UTC / TAI, via several European UTC(k) laboratories.
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Monday, 22 March 2010
7
CLOCK AND TIME TRANSFER NOISE MODELS
• Clock noise is modelled using a linear combination of Integrated Markov Noise Processes
• Close to FFM in the case of Active Hydrogen Masers • Close to WFM in the case of Caesium clocks
• Time transfer noise is modelled as linear combination of Markov Noise Processes
• Part way in characteristics between WPM and FPM, stationary in long term
• Similar model for TWSTFT, GPSCV and internal Galileo measurements
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Monday, 22 March 2010
8
NOISE MODELS FOR CAESIUM CLOCKS AND ACTIVE HYDROGEN MASERS
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Monday, 22 March 2010
9
NOISE MODELS FOR TWSTFT AND GPS CV MEASUREMENT NOISE
5 5.0005 5.001 5.0015 5.002
x 104
-1
0
1
2
3
4
5
6
7
8
9x 10
-9 Tim e Tra ns fe r Nois e S im ula tion
MJ D
Tim
e O
ffset
GP S CV TWS TFT
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Monday, 22 March 2010
10
TWSTFT MODEL, ADEV, MDEV, HDEV CHARACTERISTICS
1 0 2
1 0 3
1 04
1 0 5
1 0 6
1 0 7
1 0 - 1 7
1 0 - 1 6
1 0 - 1 5
1 0 - 1 4
1 0 - 1 3
1 0 - 1 2 A D E V , H D E V M D E V T W S T F T
ta u
AD
EV /
MD
EV /
HD
EV
A D E V s im u la t io nH D E V s im u la t io nM D E V s im u la t io nA D E V e x p e c ta t io nH D E V e x p e c ta t io n M D E V e x p e c ta t io n
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Monday, 22 March 2010
11
GPS CV MODEL, ADEV, MDEV, HDEV CHARACTERISTICS
1 0 2
1 03
1 04
1 0 5
1 06
1 071 0
- 1 6
1 0- 1 5
1 0- 1 4
1 0- 1 3
1 0- 1 2 A D E V , H D E V M D E V G P S C V
ta u
AD
EV /
MD
EV /
HD
EV
A D E V s im u la t io nH D E V s im u la t io nM D E V s im u la t io nA D E V e x p e c ta t io nH D E V e x p e c t a t io nM D E V e x p e c t a t io n
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Monday, 22 March 2010
12
ENSEMBLE ALGORITHMS
• Aim is to produce a “composite” free running timescale from an ensemble of individual clocks
• Realised via estimates of (CClk – CI )• Early algorithms based on weighted mean
approach• Later algorithms based on use of Kalman
Filter, these are used in Galileo
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Monday, 22 March 2010
13
ENSEMBLE ALGORITHM• TSP ensemble algorithm will include clocks from
several UTC(k) laboratories.
• Time Offset, Normalised Frequency Offset, Linear Frequency Drift and Integrated Markov Process state vector components for each clock
• Active hydrogen masers assumed to possess linear frequency drift, caesium clocks assumed to be free of linear frequency drift
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Monday, 22 March 2010
14
ENSEMBLE ALGORITHM
• Long term, ensemble timescale will be free of linear frequency drift
• Close to optimal stability at all averaging times
• Includes linear frequency drift in clock models
• May include white measurement noise
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Monday, 22 March 2010
15
COMBINING CLOCKS WITH DIFFERENT STABILITY CHARACTERISTICS
2 2 . 5 3 3 . 5 4 4 . 5 5 -1 5 . 8
-1 5 . 6
-1 5 . 4
-1 5 . 2
-1 5
-1 4 . 8
-1 4 . 6
-1 4 . 4
-1 4 . 2
L o g 1 0 ( τ )
Log 10
(σy)
C lo c k 1 (F F M ) C lo c k 2 (W F M ) C lo c k 3 (W F M ) C o m p o s ite S im p le C o m p o s ite O p t im a l C o m p o s ite
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Monday, 22 March 2010
16
ENSEMBLE ALGORITHM PERFORMANCE, 2 HYDROGEN MASERS + 4 CAESIUM CLOCKS
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Monday, 22 March 2010
17
WHITENESS OF RESIDUALS
5.0008 5.0008 5.0009 5.0009 5.001
x 104
-6
-4
-2
0
2
4
6 x 10
-12 Clock 2 - Clock1, Res idua ls ,
MJD
Res
idua
ls
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Monday, 22 March 2010
18
REMAINING ISSUES
• Treatment of non-white measurement noise
• Difference in noise levels between and within laboratories.
• Biases in time transfer measurements
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Monday, 22 March 2010
19
TIME TRANSFER COMBINING ALGORITHMS
• Use range of satellite time transfer measurements to estimate offset between two distant timescales
• UTC(j) – UTC(k) links
• Offset between two separate PTF reference timescales
• GST(MC) – GPS_Time offset
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Monday, 22 March 2010
20
ALGORITHM PROPERTIES
• Kalman filter based• Multiple input data sets in single algorithm:
TWSTFT, GPSCV, internal Galileo products • Use of both TWSTFT and GPSCV calibration
within algorithm• Model calibration offset as long relaxation time
Markov Process • Timescale and time transfer models already
described
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Monday, 22 March 2010
21
ALGORITHM PERFORMANCE
• Separately weight short term noise and calibration uncertainties of TWSTFT and GPSCV data
• Maintain TWSTFT calibration uncertainty on loss of TWSTFT data
• Good performance in case of missing data
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Monday, 22 March 2010
22
ESTIMATION PERFORMANCE OF TIME TRANSFER COMBINING ALGORITHM
5 5 .0 0 0 5 5 .0 0 1 5 .0 0 1 5 5 .0 0 2
x 1 04
-0 .5
0
0 .5
1 .0
1 .5
2 x 1 0
-9 T im e O ffs e t E s tim a tio n E rro r
M J D
Tim
e O
ffset
T im e O ffs e t E rro r T im e O ffs e t E rro r E xp e c ta tio n
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Monday, 22 March 2010
23
STEERING AND PREDICTION ALGORITHMS
• Based on time transfer combining algorithms
• Kalman filter based timescale predictor added to deal with delays in data set availability
• Steering algorithm added, to either minimise time offset or frequency offset
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Monday, 22 March 2010
24
PERFORMANCE OF A STEERING ALGORITHM
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Monday, 22 March 2010
25
FIRST RESULTS FROM TSP
Steered and unsteered timescales
-8.00E-08
-6.00E-08
-4.00E-08
-2.00E-08
0.00E+00
2.00E-08
54400 54500 54600 54700 54800 54900
MJD
Tim
e O
ffset
steered unsteered
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Monday, 22 March 2010
26
CONCLUSIONS
• NPL has developed a range of state of the art timescale algorithms
• Currently being developed for use in Galileo reference timescale.
• First TSP results within specification achieved