Time Transfer – Algorithms for the Galileo Programme · Time Transfer – Algorithms for the...

Time Transfer – Algorithms for the Galileo Programme

John Davis 3rd June 2009

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.


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)


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


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


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.


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


8

NOISE MODELS FOR CAESIUM CLOCKS AND ACTIVE HYDROGEN MASERS


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


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


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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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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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


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


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


16

ENSEMBLE ALGORITHM PERFORMANCE, 2 HYDROGEN MASERS + 4 CAESIUM CLOCKS


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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REMAINING ISSUES

• Treatment of non-white measurement noise

• Difference in noise levels between and within laboratories.

• Biases in time transfer measurements


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


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


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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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


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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PERFORMANCE OF A STEERING ALGORITHM


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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


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

Time Transfer – Algorithms for the Galileo Programme · Time Transfer – Algorithms for the...

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