Skip to main content
SpringerLink
Log in

Time series analysis of carrier phase differences for dual-frequency GPS high-accuracy positioning

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The GPS position based on carrier phase measurement is produced and developed for the exaltation of positioning accuracy. However, in practice, carrier phase observations often include various errors besides essential parameters. Sometimes even satellite signals may be temporarily interrupted and lead to cycle slips. Most of errors can be eliminated with differencing of carrier phase observation. But phase center offsets (PCOs) are a key source of errors in GPS precise measurements and hardly eliminated or weakened by difference methods. Meanwhile as a gross error, cycle slips decreases badly positioning accuracy but can’t remove by differencing approach. For all this, based on the feature of cycle slips and time series analysis theory, a quickly and convenient method of eliminating various errors, detecting and correcting cycle slips and PCOs is presented in this paper for high accuracy positioning solution. The key of this method is to build difference series between carrier phase observations of satellites, stations and epochs. These difference series can be used to eliminate various errors implied in observation data, and highlight the features of cycle slips. Based on the time-series analysis theory, the model of difference series of carrier phase observation data is built, cycle slips can be quickly detected by the stability of this time series. At last, the experiment results show that the new method can not only be suitable both for static and dynamic measurements of dual-frequency GPS receivers, but also can eliminate most measurement errors of dual-frequency GPS receivers, improve the efficiency of detection and correction of PCOs and cycle slips less than 1 cycle.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Kaplan, E.D., Hegarty, J.: Understanding GPS: Principles and Applications, 2nd edn. Artech House, London (2006)

    Google Scholar 

  2. Boehm, J., Werl, B., Schuh, H.: Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. J. Geophys. Res. 111(B2), 1–9 (2006)

    Article  Google Scholar 

  3. Capua, R.: GPS, Galileo and the future of high precision services: an interoperability point of view. In: Re, E.D., Ruggieri, M. (eds.) Satellite Communications and Navigation Systems, pp. 481–494. Springer, Boston (2008)

    Chapter  Google Scholar 

  4. Aguado, L., et al.: A low-cost, low-power Galileo/GPS positioning system for monitoring landslides. In: Navitec (2006)

  5. Quddus, M.A., Noland, R.B., Ochieng, W.Y.: The effects of navigation sensors and spatial road network data quality on the performance of map matching algorithms. GeoInformatica 13(1), 85–108 (2009)

    Article  Google Scholar 

  6. Kis, L., Lantos, B.: Development of state estimation system with INS, magnetometer and carrier phase GPS for vehicle navigation. Gyroscopy Navig. 5(3), 153–161 (2014)

    Article  Google Scholar 

  7. Belakbir, A., Amghar, M., Sbiti, N.: Sensor data fusion for an indoor and outdoor localization. Radioelectron. Commun. Syst. 57(4), 149–158 (2014)

    Article  Google Scholar 

  8. Buchli, B., Sutton, F., Beutel, J.: GPS-equipped wireless sensor network node for high-accuracy positioning applications. In: Picco, G.P., Heinzelman, W. (eds.) Wireless Sensor Networks: 9th European Conference, pp. 179–95 (2012)

  9. Lee, J.Y., Kim, H.S., Choi, K.H., Lim, J., Chun, S., Lee, H.K.: Adaptive GPS/INS integration for relative navigation. GPS Solut. 20(1), 63–75 (2016)

    Article  Google Scholar 

  10. Zhang, X., Guo, B., Guo, F., Du, C.: Influence of clock jump on the velocity and acceleration estimation with a single GPS receiver based on carrier-phase-derived Doppler. GPS Solut. 17(4), 549–559 (2013)

    Article  Google Scholar 

  11. Angrisano, A., Gaglione, S., Troisi, S.: Real-time receiver clock jump detection for code absolute positioning with Kalman filter. Wireless Pers. Commun. 79(1), 211–221 (2014)

    Article  Google Scholar 

  12. Misra, P., Enge, P.: Global positioning system: Signals, measurements, and performance (Chinese edition). Publishing House of Electronics Industry, Beijing (2008)

    Google Scholar 

  13. Chen, Y., Zhao, S., Farrell, J.A.: Computationally efficient carrier integer ambiguity resolution in multiepoch GPS/INS: a common-position-shift approach. IEEE Trans. Control. Syst. Technol. 99, 1–16 (2015)

    Google Scholar 

  14. Li, C.: Research and application of carrier phase in improving the positioning accuracy in GNSS receiver. Beijing University of Post and Telecommunications, Beijing, China, Thesis paper (2013)

  15. Tian, S., Dai, W., Liu, R., Chang, J., Li, G.: System using hybrid LEO-GPS satellites for rapid resolution of integer cycle ambiguities. IEEE Trans. Aerosp. Electron. Syst. 50(3), 1774–1785 (2014)

    Article  Google Scholar 

  16. Yi, T., Li, H., Yi, X., Wang, G.: Cycle slip detection and correction of GPS carrier phase based on wavelet transform and neural network. Chin. J. Sens. Actuators 20(4), 897–902 (2007)

    Google Scholar 

  17. Zhang, C., Xu, Q., Li, Z.: Improving method of cycle slip detection and correction based on combination of GPS pseudo range and carrier phase observations. Acta Geodaetica et Cartographica Sinica 38(5), 402–407 (2009)

    MathSciNet  Google Scholar 

  18. Zhou, W., Hao, J., Jia, X.: The application of some methods on cycle slip detection in COMPASS data preprocessing. Eng. Surv. Mapp. 18(2), 66–69 (2009)

    Article  Google Scholar 

  19. Zhang, S.: GPS Tech. Appl. China National Defense Industry Press, Beijing (2004)

    Google Scholar 

  20. Ince, C.D., Sahin, M.: Real-time deformation monitoring with GPS and Kalman filter. Earth Planets Space 52(10), 837–840 (2000)

    Article  Google Scholar 

  21. Liu, Z.: A new automated cycle slip detection and repair method for a single dual-frequency GPS receiver. J. Geodesy 85(3), 171–183 (2011)

    Article  Google Scholar 

  22. Fu, L., Wang, L., Hu, J., Liu, X.: Stability analysis of inertial navigation system-aided phase-lock-loop via an integral quadratic constraint approach. IET Radar Sonar Navig. 8(9), 1100–1108 (2014)

    Article  Google Scholar 

  23. Schüler, T., Diessongo, H., Poku-Gyamfi, Y.: Precise ionosphere-free single-frequency GNSS positioning. GPS Solut. 15, 139–147 (2011)

    Article  Google Scholar 

  24. Klobuchar, J.A., Kunches, J.M.: Comparative range delay and variability of the earth’s troposphere and the ionosphere. GPS Solut. 7(1), 55–8 (2003)

    Article  Google Scholar 

  25. Liu, X., Tiberius, C., de Jong, K.: Modelling of differential single difference receiver clock bias for precise positioning. GPS Solut. 7(4), 209–221 (2004)

    Article  Google Scholar 

  26. Wang, G., Wang, Z., Yin, H.: An cycle-slip correction method for real-time kinematic GPS data based on triple differences observation. Geomat. Inf. Sci. Wuhan Univ. 32(8), 711–714 (2007)

    Google Scholar 

  27. Jia, P., Wu, L.: An algorithm for detecting and estimating cycle slips in single-frequency GPS. Chin. Astron. Astrophys. 25, 515–521 (2001)

    Article  Google Scholar 

  28. Schenk, V., Schenková, Z., Bosy, J., Kontny, B.: Reliability of GPS data for geodynamic studies case study: Sudeten area. The Bohemian Massif. Acta Geodyn. Geomater. 7, 113–128 (2010)

    Google Scholar 

  29. Stępniak, K., Wielgosz, P., Baryła, R.: Field tests of L1 phase centre variation models of surveying-grade GPS antennas. Stud. Geophys. Geod. 59(3), 394–408 (2015)

    Article  Google Scholar 

  30. Alvaro, S.: Very short baseline interferometry: assessment of the relative stability of the GPS stations at the Yebes Observatory (Spain). Stud. Geophys. Geod. 57(2), 233–252 (2013)

    Article  Google Scholar 

  31. Li, X., Wang, X., Ren, J.: Research on calibration methods of GNSS antenna phase center offsets and variations. Prog. Astron. 30(4), 501–517 (2012)

    MathSciNet  Google Scholar 

  32. Rothacher, M.: Comparison of absolute and relative antenna phase center variations. GPS Solut. 4, 55–60 (2001)

    Article  Google Scholar 

  33. Baire, Q., Bruyninx, C., Legrand, J., Pottiaux, E., Aerts, W., Defraigne, P., Bergeot, N., Chevalier, J.M.: Influence of different GPS receiver antenna calibration models on geodetic positioning. GPS Solut. 18(4), 529–539 (2014)

    Article  Google Scholar 

  34. Jia, Z., Chen, Z., Yu, P., Lin, M.: Relative positioning calibration method of phase center offsets of GPS signal antennas. Gyroscopy Navig. 3, 1–7 (2016)

    Google Scholar 

  35. Mittelhammer, R.C.: Hypothesis Testing Methods and Confidence Regions. Mathematical Statistics for Economics and Business, pp. 609–695. Springer, New York (2013)

    Google Scholar 

  36. Bagnall, A., Janacek, G.: A run length transformation for discriminating between auto regressive time series. J. Classif. 31(2), 154–178 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  37. Yu, N., Yi, D., Tu, X.: Analyze auto-correlations and partial-correlations function in time series. Math. Theory Appl. 27(1), 54–57 (2007)

    MathSciNet  Google Scholar 

  38. Isermann, R., Münchhof, M.: Least Squares Parameter Estimation for Dynamic Processes. Identification of Dynamic Systems: An Introduction with Applications. Springer, Berlin (2011)

    MATH  Google Scholar 

  39. Luo, F., Dai, W., Wu, X.: EMD filtering based on cross-validation and its application in GPS multipath. Geomat. Inf. Sci. Wuhan Univ. 37(4), 450–453 (2012)

    Google Scholar 

  40. Li, J., Li, Y., Zhou, Y.: GPS multipath mitigation algorithm using C/A code correlation character. In: Proceedings of the Second International Conference Communications, Signal Processing, and Systems, pp. 1047–1058 (2014)

  41. Xu, Z., et al.: Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput. doi:10.1109/TCC.2016.2517638

  42. Xu, Z., Zhang, H., Sugumaran, V., Raymond Choo, K.-K., Mei, L., Zhu, Y.: Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP J. Wireless Commun. Netw. 2016, 44 (2016)

    Article  Google Scholar 

  43. Xu, Z., Zhang, H., Hu, C., Mei, L., Xuan, J., Raymond Choo, K.-K., Sugumaran, V., Zhu, Y.: Building knowledge base of urban emergency events based on crowdsourcing of social media, Concurr. Comput. Pract. Exp. doi:10.1002/cpe.3780

Download references

Acknowledgments

This study was supported by the Director Fund of China Earthquake Administration (Major Research Project, No. IS201426145) and the National Natural Science Foundation of China (No. 41504033).

Author information

Authors and Affiliations

  1. Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, 40 Hongshance Road, Wuhan, 430071, China

    Zhige Jia & Zhengsong Chen

  2. Wuhan Base of Institute of Crustal Dynamics, China Earthquake Administration, 48 Hongshance Road, Wuhan, 430071, China

    Dijin Wang & Pengfei Yu

Authors
  1. Zhige Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengsong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dijin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengfei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhige Jia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, Z., Chen, Z., Wang, D. et al. Time series analysis of carrier phase differences for dual-frequency GPS high-accuracy positioning. Cluster Comput 19, 1461–1474 (2016). https://doi.org/10.1007/s10586-016-0607-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0607-4

Keywords

Search

Navigation