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Efficient frequency jumps detection algorithm for atomic clock comparisons

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper a new method of frequency jumps detection in data from atomic clock comparisons is proposed. The presented approach is based on histogram analysis for different time intervals averaging phasetime data recorded over a certain period of time. Our method allows identification of multiple frequency jumps for long data series as well to almost real-time jump detection in combination with advanced filtering. Several methods of preliminary data processing have been tested (simple averaging, moving average and Vondrak filtration), to achieve flexibility in adjusting the algorithm parameters for current needs which is the key to its use in determining ensemble time scale or to control systems of physical time scales, such as UTC(PL). The best results have been achieved with the Vondrak filter.
Rocznik
Strony
107--121
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr., wzory
Twórcy
  • National Institute of Telecommunications, Szachowa 1, 94-894 Warsaw, Poland
  • National Institute of Telecommunications, Szachowa 1, 94-894 Warsaw, Poland
autor
  • National Institute of Telecommunications, Szachowa 1, 94-894 Warsaw, Poland
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Electronic Systems, Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
  • [1] Davis, J. A., Shemar, S. L., & Whibberley, P. B. (2011). A Kalman filter UTC(k) prediction and steering algorithm. Joint Conference of the IEEE International Frequency Control and the European Frequency and Time Forum (FCS) Proceedings, USA. https://doi.org/10.1109/FCS.2011.5977793
  • [2] Thomas, C., Wolf, P., & Tavella, P. (1994).Time Scales, BIPM.
  • [3] Riley, W. J. (2013). Outliers in Time and Frequency Measurements. Hamilton Technical Services.
  • [4] Ruusunen, M., Paavola, M., Pirttimaat, M., & Leiviskai, K. (2005). Comparison of three change detection algorithms for an electronics manufacturing process. IEEE International Symposium on Computational Intelligence in Robotics and Automation, Finland, 679-683. https://doi.org/10.1109/CIRA.2005.1554355
  • [5] Riley, W. J. (2006). Gaps, Outliers, Dead Time, and Uneven Spacing in Frequency Stability Data. Hamilton Technical Services.
  • [6] Marszalec, M., & Lusawa, M. (2014). Research on impact of the environmental factors on National Institute of Telecommunications time standards stability. Proceedings of SPIE 9290, Poland. https://doi.org/10.1117/12.2076058
  • [7] Riley, W. J. (2010). Frequency Jump Detection and Analysis. Proceedings of 40th Annual Precise Time and Time Interval (PTTI) Meeting, USA, 241-253.
  • [8] Nunzi, E., Galleani, L., Tavella, P., & Carbone, P. (2007). Detection of Anomalies in the Behavior of Atomic Clocks. IEEE Transactions on Instrumentation and Measurement, 56(2), 523-528. https://doi.org/10.1109/TIM.2007.891118
  • [9] Galleani, L., & Tavella, P. (2010). An algorithm for the detection of frequency jumps in space clocks. Proceedings of 42nd Annual Precise Time and Time Interval (PTTI) Meeting, USA, 503-508.
  • [10] Galleani, L., & Tavella, P. (2012). Detection of atomic clock frequency jumps with the Kalman filter. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 59(3), 503-508. https://doi.org/10.1109/TUFFC.2012.2221
  • [11] Huang, X., Gong, H., & Ou, G. (2014). Detection of weak frequency jumps for GNSS onboard clocks. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 61(5), 747-755. https://doi.org/10.1109/TUFFC.2014.2967
  • [12] Galleani, L., & Tavella, P. (2017). Robust Detection of Fast and Slow Frequency Jumps of Atomic Clocks. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 64(2), 475-485. https://doi.org/10.1109/TUFFC.2016.2625311
  • [13] Feissel, M., & Lewandowski, W. (1984). A comparative analysis of Vondrak and Gaussian smoothing techniques. Journal of Geodesy, 58(4), 464-474.
  • [14] Vondrák, J. (1969). A contribution to the problem of smoothing observational data. Bulletin of the Astronomical Institute of Czechoslovakia, 20, 349-359.
  • [15] Vondrák, J. (1977). Problem of smoothing observational data II. Bulletin of the Astronomical Institute of Czechoslovakia, 28, 84-89.
  • [16] Štefka, V., & Pesek, I. (2007). Implementation of the Vondrak’s smoothing in the combination of results of different space geodesy techniques. Acta Geodynamica et Geomaterialia, 4(4), 129-132.
  • [17] International Telecommunication Union. (2017). Measures for random instabilities in frequency and time (phase) (Recommendation ITU-R TF.538-4). https://www.itu.int/dms_pubrec/itu-r/rec/tf/R-REC-TF.538-4-201707-I!!PDF-E.pdf
  • [18] Gardiner, V., & Gardiner, G. (1979). Analysis of Frequency Distributions. Concepts and Techniques in Modern Geography, 19.
  • [19] Premoli, A., & Tavella, P. (1993). A revisited three-cornered hat method for estimating frequency standard instability. IEEE Transactions on Instrumentation and Measurement, 42(1), 7-13. https://doi.org/10.1109/19.206671
Uwagi
1. This work was supported by the Polish Ministry of Science and Higher Education under the project of Special Research Equipment support “Assembly of Time and Frequency Standards for participation in the Polish Atomic Timescale TA(PL)” (dec. no. 8/E-242/SPUB/SN/2019 and 6/E-242/SPUB/SN/2020).
2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2b162fbd-7e81-415c-aaa8-3ef8b3c9efed
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