Skip to main content

Advertisement

Log in

A wavelet-based hybrid approach to remove the flicker noise and the white noise from GPS coordinate time series

  • Original Article
  • Published:
GPS Solutions Aims and scope Submit manuscript

Abstract

Understanding the destructive interference of noise involved in GPS coordinate time series is crucial for improving the reliability of GPS applications. The majority of the noise consists of both flicker and white noise, both of which are well characterized by a stochastic process following a power-law noise model. To simplify the noise removal for GPS coordinate time series, the noise is usually regarded as pure white noise rather than a mixture of flicker noise and white noise. This work proposes a wavelet-based integrated solution that merges the strengths of Shannon entropy and wavelet thresholding to remove flicker and white noise at the same time. A GPS coordinate time series, spanning 128 days from the GPS monitoring station at the Jinduicheng Mine in Shanxi, China, was selected to test the proposed algorithm. The results demonstrate that both flicker noise and white noise are worthy of attention because they can lead to a seriously misunderstandings about error in a GPS coordinate time series. The utility of our proposed algorithm in removing flicker and white noise is shown to be more comprehensive than the use of wavelet thresholding alone. The findings further reveal that the advance elimination of flicker noise is beneficial for subsequently utilizing wavelet thresholding to delete the white noise in a GPS coordinate time series. This will greatly improve the reliability of GPS coordinate time series, allowing such data to be applied to a wide range of fields.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Agnew DC (1992) The time-domain behavior of power-law noises. Geophys Res Lett 19(4):333–336

    Article  Google Scholar 

  • Amiri-Simkooei AR (2009) Noise in multivariate GPS position time-series. J Geodesy 83(2):175–187

    Article  Google Scholar 

  • Amiri-Simkooei AR, Tiberius CCJM (2007) Assessing receiver noise using GPS short baseline time series. GPS Solut 11(1):21–35

    Article  Google Scholar 

  • Amiri-Simkooei AR, Tiberius CCJM, Teunissen PJG (2007) Assessment of noise in GPS coordinate time series: methodology and results. J Geophys Res 112:B07413. doi:10.1029/2006JB004913

    Google Scholar 

  • Azzalini A, Farge M, Schneider K (2005) Nonlinear wavelet thresholding: a recursive method to determine the optimal denoising threshold. Appl Comput Harmon Anal 18(2):177–185

    Article  Google Scholar 

  • Chen BS, Lin CW (1994) Multiscale Wiener filter for the restoration of fractal signals: wavelet filter bank approach. IEEE Trans Signal Process 42(11):2972–2982

    Article  Google Scholar 

  • Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627

    Article  Google Scholar 

  • Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455

    Article  Google Scholar 

  • Fu Z (2001) Information theory-fundamental theory and application. Publishing House Of Electronics Industry, Beijing

    Google Scholar 

  • Gao HY (1997) Choice of thresholds for wavelet shrinkage estimate of the spectrum. J Time Ser Anal 18(3):231–251

    Article  Google Scholar 

  • Geng Y, Wang J (2008) Adaptive estimation of multiple fading factors in Kalman filter for navigation applications. GPS Solut 12(4):273–279

    Article  Google Scholar 

  • Han M, Liu Y, Xi J, Guo W (2007) Noise smoothing for nonlinear time series using wavelet soft threshold. IEEE Signal Process Lett 14(1):62–65

    Article  Google Scholar 

  • He K, Wang SX (2003) Study on denoising of fractal signal based on Shannon entropy. In: Proceedings of 2003 International Conference on Neural Networks and Signal Processing, vols 1 and 2. IEEE, New York, pp 751–755

  • Huang L, Fu Y (2007) Analysis on the noises from continuously monitoring GPS sites. Acta Seismol Sin 29(2):197–202

    Google Scholar 

  • Langbein J, Johnson H (1997) Correlated errors in geodetic time series: implications for time-dependent deformation. J Geophys Res 102(B1):591–603

    Article  Google Scholar 

  • Leick A (2004) GPS satellite surveying. Wiley, Hoboken

    Google Scholar 

  • Ma L, Shi Y, Yu H (2002) Studying on denoising of chaotic signal using wavelet transform. Signal Process 18(1):83–87

    Google Scholar 

  • Mandelbrot BB (1983) The fractal geometry of nature. W.H. Freeman, New York

    Google Scholar 

  • Mao A, Harrison CG, Dixon TH (1999) Noise in GPS coordinate time series. J Geophys Res 104(B2):2797–2816

    Article  Google Scholar 

  • Montillet JP, Tregoning P, McClusky S, Yu K (2013) Extracting white noise statistics in GPS coordinate time series. IEEE Geosci Remote Sens Lett 10(3):563–567

    Article  Google Scholar 

  • Nikolaidis R, Bock Y, de Jonge PJ, Shearer P, Agnew DC, Domselaar M (2001) Seismic wave observations with the global positioning system. J Geophys Res 106(B10):21897–21916

    Article  Google Scholar 

  • Niu X, Chen Q, Zhang Q, Zhang H, Niu J, Chen K, Shi C, Liu J (2014) Using Allan variance to analyze the error characteristics of GNSS positioning. GPS Solut 18(2):231–242

    Article  Google Scholar 

  • Santamaria-Gomez A, Bouin M-N, Collilieux X, Woeppelmann G (2011) Correlated errors in GPS position time series: implications for velocity estimates. J Geophys Res 116:B01405. doi:10.1029/2010jb007701

    Google Scholar 

  • Senior KL, Ray JR, Beard RL (2008) Characterization of periodic variations in the GPS satellite clocks. GPS Solut 12(3):211–225

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  Google Scholar 

  • Shi C, Gu S, Lou Y, Ge M (2012) An improved approach to model ionospheric delays for single-frequency precise point positioning. Adv Space Res 49(12):1698–1708

    Article  Google Scholar 

  • Shi C, Yi W, Song W, Lou Y, Zhang R (2013) GLONASS pseudorange inter-channel biases and their effects on combined GPS/GLONASS precise point positioning. GPS Solut 17(4):439–451

    Article  Google Scholar 

  • Souza E, Monico J (2004) Wavelet shrinkage: high frequency multipath reduction from GPS relative positioning. GPS Solut 8(3):152–159

    Article  Google Scholar 

  • Teferle FN, Williams SDP, Kierulf HP, Bingley RM, Plag HP (2008) Acontinuous GPS coordinate time series analysis strategy for highaccuracy vertical land movements. Phys Chem Earth 33(3):205–216

    Article  Google Scholar 

  • Tregoning P, Watson C (2011) Correction to “Atmospheric effects and spurious signals in GPS analyses”. J Geophys Res 116(B2):B02412. doi:10.1029/2010JB008157

    Google Scholar 

  • Wang Z, Zhang H, Sun J (2011) Framework and case study on geospatial knowledge discovery based on spatial knowledge management. In: Eighth international conference, fuzzy systems and knowledge discovery (FSKD). IEEE, Shanghai, pp 1280–1284. doi:10.1109/FSKD.2011.6019709

  • Williams SDP (2008) CATS: GPS coordinate time series analysis software. GPS solut 12(2):147–153

    Article  Google Scholar 

  • Williams SDP, Bock Y, Fang P, Jamason P, Nikolaidis PM, Prawirodirdjo M, Miller M, Johnson DJ (2004) Error analysis of continuous GPS position time series. J Geophys Res 109(B3):B03412. doi:10.1029/2003JB002741

    Google Scholar 

  • Wornell GW, Oppenheim AV (1992) Estimation of fractal signals from noisy measurements using wavelets signal processing. IEEE Trans Signal Process 40(3):611–623

    Article  Google Scholar 

  • Wu H, Li Y, Chen N, Xiao S, Cui W (2011) A grid-based remote monitoring and diagnosis system for the railway roadbed roller compaction. Adv Sci Lett 4(8–10):3233–3237

    Article  Google Scholar 

  • Wu H, Tao J, Li X, Chi X, Li H, Yang R, Wang S, Chen N (2013) A location based service approach for collision warning systems in concrete dam construction. Saf Sci 51(1):338–346

    Article  Google Scholar 

  • Zhang Q, Aliaga-Rossel R, Choi P (2006) Denoising of gamma-ray signals by interval-dependent thresholds of wavelet analysis. Meas Sci Technol 17(4):731–735

    Article  Google Scholar 

  • Zhao G, Pei H, Liang H (2013) Measurement of additional strains in shaft lining using differential resistance sensing technology. Int J Distrib Sens Netw. doi:10.1155/2013/153834

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (40901214 and 41301588), the China Postdoctoral Science Foundation (2013M 531749, 2012T50691), the Hong Kong Scholars Program (XJ2012036), the Hong Kong Polytechnic University under Projects (G-YZ26), the Fundamental Research Funds for the Central Universities (2013-IV-040), and the self-determined and innovative research funds of WUT (20131049708009). Additionally, we are grateful to American Journal Experts for English editing of the manuscript. The helpful comments and suggestions from two anonymous reviewers are very gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenzhong Shi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Li, K., Shi, W. et al. A wavelet-based hybrid approach to remove the flicker noise and the white noise from GPS coordinate time series. GPS Solut 19, 511–523 (2015). https://doi.org/10.1007/s10291-014-0412-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10291-014-0412-6

Keywords

Navigation