Development of Algorithms for use in the Galileo Time Service Provider

Transcription

1 Development of Algorithms for use in the Galileo Time Service Provider D Baines 1, J A Davis 1, G Parkin 1, P Harris 1, A Batchelor 2, J. M. Pieplu 3, J. H. Hahn 4, A Bauch 5, V. Pettiti 6, P. Uhrich 7, R. Jones 8, S. Bedrich 9, T. Levin 9, M. Stevens 1 1 National Physical Laboratory, Hampton Road, Teddington, Middlesex, UK 2 Thales Research and Technology, Worton Drive, Reading, UK 3 European GNSS Supervisory Authority, Brussels, Belgium 4 Galileo Project Office, European Space Agency, ESTEC, Noordwijk, The Netherlands 5 Physikalisch-Technische Bundesanstalt, Braunschweig,Germany 6 Istituto Nazionale di Ricerca Metrologica, Torino, Italy 7 LNE-SYRTE, UMR CNRS 8630, Observatoire de Paris, Paris, France 8 Helios Technology Ltd,Aerospace Boulevard, Farnborough, UK 9 Kayser-Threde GmbH, Munich, Germany

2 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 2

3 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 3

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 1x Requirements depend on the performance of both the PTF and TSP. 4

5 The Prototype TSP Awarded to the Fidelity consortium: Contains 4 core UTC(k) labs, INRiM, NPL, OP and PTB. 5

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 C TSP and steered timescale C TSPS 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. 6

7 TSP Interfaces Friday, 23 May 2008 Galileo Rx GPS Rx TWSTFT TX/Rx Galileo Rx GPS Rx TWSTFT TX/Rx UTC(k) UTC(k) Lab UTC(k) Lab Lab Galileo Galileo PTF PTF GSS Galileo Timing Users Time Service Provider Data pre-processing Data Archiving TAI Prediction EGNOS Loran-C BIPM 7

8 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 8

9 The Algorithms TSP Verification Segment Main flow MMI Control Segment Verification Segment In/out DB Alarm GMS BIPM UTC(k) PP GPS PP TWS XINT DD PP CLK Control CCLK Segment TPRED STEER MSG XINT PP GAL GMS BIPM UTC(k) WWW Friday, 23 May 2008 PPCLK: PPTWSTFT: TPREDSTEER: PPGPS: CCLK: Clock Prediction Pre-process Data Composite and Clock Steering GPS TWSTFT Data algorithm Data Algorithm Compute Detect anomalies. internal noise freerunning timescale ensemble TT links. C steered Compute TT parameters Provide PPGPS & TSPS monthly Validate ADEV timescale & predict (GPSCV with processed of clocks. C TSP from (UTC-GST(MC)) -TWSTFT) Provide max of TWSTFT CCLK 100 clocks. for up to differences. data. with Deal 50 days. (UTC(k) with clock Daily CI) Provide estimation CCLK & & frequency (GST(MC) steers. prediction TT Cl) with TT of results. data. Compute ADEV (UTC-GST(MC)). Compute stabilities daily of steering C TSP. parameters Provide to results steer to GST(MC) TPREDSTEER. to UTC. 9

10 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 10

11 Functionality of Pre-Processing elements Friday, 23 May 2008 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. 11

12 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 12

13 PPCLK Example of real data run through PPCLK with examples of anomalies: INRiM clock data from 16 th Nov 2007 to 6 th Dec 2007, MJD to MJD Types of anomalies: Outliers, Noise parameter anomalies (Validating that the noise model used for each clock is physically realistic), diurnal instabilities, bad clocks. 13

14 PPCLK - inputs 8 x 10-5 UTC - CI Raw Measurements Friday, 23 May 2008 Clock Offset Time offset anomaly Clock 1 Clock 2 Clock 3 Clock 4 Clock 5 Clock 6 Clock 7 Bad clock (H maser failing on MJD 54431) MJD x

15 PPCLK - outputs 8 x 10-5 UTC - CI Pre-Processed Measurements Friday, 23 May 2008 Clock Offset Clock 1 Clock 2 Clock 3 Clock 4 Clock 5 Clock 6 Clock 7 Bad clock identified and removed Time offset identified and removed MJD x

16 PPCLK - inputs Friday, 23 May 2008 UTC - CI Raw measurements; Number of Good Points 26 Number Of Good Points Clock 1 Clock 2 Clock 3 Clock 4 Clock 5 Clock 6 Clock MJD x

17 PPCLK - outputs Friday, 23 May 2008 UTC - CI Pre-Processed measurements; Number of Good Points 25 Number Of Good Points Clock 1 Clock 2 Clock 3 Clock 4 Clock 5 Clock 6 Clock MJD x

18 PPCLK detecting the anomalies, before x 10-5 Kalman Filter Residuals Residuals, Clock 3 ( ) - Clock 6 ( ) Friday, 23 May 2008 Residual Offset Time offset anomaly in Clock MJD x

19 PPCLK detecting the anomalies, before x 10-5 Kalman Filter Residuals Residuals, Clock 3 ( ) - Clock 6 ( ) Friday, 23 May 2008 Residual Offset Zooming in on anomaly detection MJD 19

20 PPCLK detecting the anomalies, after x 10-9 Kalman Filter Residuals Residuals, Clock 3 ( ) - Clock 6 ( ) Friday, 23 May 2008 Residual Offset Threshold set to approx 5σ, identifies anomaly as offset & is removed MJD x

21 Sigma PPCLK 10-12ADEV estimates from n cornered hat, INRiM clocks tau ADEV, Clock 1 ADEV, Clock 2 ADEV, Clock 3 ADEV, Clock 4 ADEV, Clock 5 ADEV, Clock 6 ADEV, Clock 7 Friday, 23 May 2008 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. 21

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). 22

23 Validating the clock noise models Daily residual Whiteness Tests Daily Whiteness Estimates, Cl 2 ( ) - Cl 3 ( ) Time Offset MJD x

24 Validating the clock noise models Daily Residual Variance Ratio Residual Variance Ratio, Cl 2 ( ) - Cl 3 ( ) 1.2 Ratio MJD x

25 PPTWSTFT: TWSTFT PTB IT link, MJD ADEV, HDEV, MDEV Estimates Time Transfer Links ADEV, HDEV, MDEV ADEV HDEV MDEV tau 25

26 PPGPS: OP NPL link, MJD ADEV, HDEV, MDEV Estimates Time Transfer Links ADEV, HDEV, MDEV ADEV HDEV MDEV Friday, 23 May tau 26

27 Presentation Introduction: the Prototype TSP The Algorithms The Pre-Processing elements Running real data through the algorithms and detecting anomalies Summary 27

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 Prototype TSP plans to be operational in early

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30 EXPECTED PERFORMANCE OF THE TSP ENSEMBLE ALGORITHM Friday, 23 May 2008 Log 10 (ADEV) Log 10 (ADEV) Caesium H Maser Optimum ensemble Log 10 (τ) Log 10 (τ) 30