SpringerLink
Log in

Improving GPS positioning accuracy using weighted Kalman Filter and variance estimation methods

  • Original Paper
  • Published:
CEAS Aeronautical Journal Aims and scope Submit manuscript

Abstract

Accurate positioning plays a crucial role in the navigation of moving objects. This paper introduces a new, fast, and accurate GPS positioning algorithm using weighting Kalman Filter (KF) based on the variance estimation method. The proposed method is evaluated using three different motion scenarios, including straight movement in the air with a velocity of 90 m/s and circular movement with velocities of 100 m/s and 500 m/s. The experimental results demonstrate that using the suggested method, the positioning accuracy for all three scenarios increases up to 30 percent as compared to two predominant methods (i.e., recursive least squares and KF).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

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

Similar content being viewed by others

References

  1. Ford, T.J., Hamilton, J.: A new positioning filter: phase smoothing in the position domain. J. Inst. Navig. 50(2), 65–78 (2003)

    Article  Google Scholar 

  2. Rahemi, N., Mosavi, M.R., Abedi, A.A., Mirzakuchaki, S.: Accurate solution of navigation equations in gps receivers for very high velocities using pseudo-range measurements. Adv. Aerosp. Eng. 2014, 1–8 (2014)

    Article  Google Scholar 

  3. Wen, Z., Henkel,P., Gunther, C., Reliable estimation of phase biases of GPS satellites with a local reference network, IEEE Conference on ELMAR, pp 321–324, 2011

  4. Rezaei, S., Sengupta, R.: Kalman filter-based integration of DGPS and vehicle sensors for localization. IEEE Trans. Control Syst. Technol. 15(6), 1080–1088 (2007)

    Article  Google Scholar 

  5. Defraigne, P., Harmegnies, A., Petit, G. Time and frequency transfer combining GLONASS and GPS Data, IEEE Joint Conference on Frequency Control and the European Frequency and Time Forum, pp 1–5, 2011

  6. Amiri-Simkooei, A.R., Teunissen, P.J.G., Tiberius, C.C.J.M.: Application of least-squares variance component estimation to GPS observables. J. Surv. Eng. 135(4), 149–160 (2009)

    Article  Google Scholar 

  7. Thipparthi, S. N. :Improving positional accuracy using carrier smoothing techniques in inexpensive gps receivers, M.Sc. Thesis, New Mexico State University (2004)

  8. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  MathSciNet  Google Scholar 

  9. Mosavi, M.R.: Comparing DGPS corrections prediction using neural network, fuzzy neural network and Kalman filter. GPS Solut. 10(2), 97–107 (2006)

    Article  Google Scholar 

  10. Yamaguchi, S, Tanaka, T.: GPS Standard Positioning using Kalman Filter, IEEE SICE-ICASE Joint Conference, pp 1351–1354, (2006)

  11. Zeng, F., Dai, W., Zhu, J., Wang, X.: Single-point positioning with the pseudo-range of single-frequency GPS considering the stochastic model. Pac. Sci. Rev. A 10(3), 274–278 (2008)

    Google Scholar 

  12. Mosavi, M.R., Nakhaei, A., Bagherinia, S.: Improvement in differential GPS accuracy using Kalman filter. J. Aerosp. Sci. Technol. 7(2), 139–150 (2010)

    Google Scholar 

  13. Liu, Y., Fan, X., Lv, C., Wu, J., Li, L., Ding, D.: An innovative information fusion method with adaptive kalman filter for integrated INS/GPS navigation of autonomous vehicles. Mech. Syst. Signal Process. 100, 605–616 (2018)

    Article  Google Scholar 

  14. Parkinson, B., Spilker, J.J., Axelrad, P., Enge, P.: GPS: Theory and Applications. AIAA, Washington (1996)

    Google Scholar 

  15. Collins, J.P., Langley, R.B.: Possible weighting schemes for GPS carrier phase observations in the presence of multipath, performing on Geodetic Research Laboratory. University of New Brunswick, Fredericton (1999)

    Google Scholar 

  16. Eueler, H.J., Goad, C.C.: On optimal filtering of GPS dual frequency observations without using orbit information. Bull. Geod. 65(2), 130–143 (1991)

    Article  Google Scholar 

  17. Hartinger, H., Brunner, F.K.: Variances of GPS phase observations: the SIGMA-ɛ model. GPS Solut. 2(4), 35–43 (1999)

    Article  Google Scholar 

  18. Wieser, A., Brunner, F.K.: An extended weight model for GPS phase observations. Earth Planet. Sp. 52(10), 777–782 (2000)

    Article  Google Scholar 

  19. Langley, R.B.: GPS receiver system noise. GPS World 8(6), 40–45 (1997)

    Google Scholar 

  20. Braasch, M.S., Dierendonck, A.J.V.: gps receiver architectures and measurements. Proc. IEEE 87(1), 48–64 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran

    Sh. Shokri, N. Rahemi & M. R. Mosavi

Authors
  1. Sh. Shokri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Rahemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. R. Mosavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. R. Mosavi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shokri, S., Rahemi, N. & Mosavi, M.R. Improving GPS positioning accuracy using weighted Kalman Filter and variance estimation methods. CEAS Aeronaut J 11, 515–527 (2020). https://doi.org/10.1007/s13272-019-00433-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13272-019-00433-x

Keywords

Search

Navigation