Using Kalman Filters on GPS Tracks

  • Krzysztof Grochla
  • Konrad Połys
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 391)


This paper describes the practical evaluation of the application of the Kalman filters to GPS tracks, gathered by mobile phones or GPS trackers. We try to answer the question whenever the filtering applied on higher layer of the mobile device software may improve the quality of the data provided by the GPS receiver. Two metrics are used for comparison: the average euclidean distance between the points on the GPS tracks and the actual location of the user and the area of the polygon created by intersection of the filtered and real track. We find that the Kalman filtering does not improve those two metrics and the direct use of the data provided by the GPS receiver provides track which is on average more near the real path than result of Kalman filtering. However we observe that this is caused by the errors introduced when the user change the direction and when we evaluate a parts of the path without rapid changes of direction (as e.g. crossing) the filters allow to generate the points which are more near the road taken by the user.


GPS tracking Kalman filtering Smartphone tracking Mobility monitoring 


  1. 1.
    Alt, H., Godau, M.: Computing the fréchet distance between two polygonal curves. Int. J. Comput. Geom. Appl 5(1–2), 75–91 (1995)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Association, N.M.E., et al.: NMEA 0183–Standard for Interfacing Marine Electronic Devices. NMEA, Severna Park (2002)Google Scholar
  3. 3.
    Babu, B.: Method and system for resolving double difference gps carrier phase integer ambiguity utilizing decentralized Kalman filters, Patent US 5,451,964 (1995)Google Scholar
  4. 4.
    Baker, D.: Portable self-contained tracking unit and GPS tracking system. Patent US 6,339,397 (2002)Google Scholar
  5. 5.
    Burkul, S.R., Pawar, P.R., Jagtap, K.R.: Estimation of vehicle parameters using Kalman filter: review. Int. J. Curr. Eng. Technol. 4(4), 2731–2735 (2014)Google Scholar
  6. 6.
    Chen, X., Wang, X., Xu, Y.: Performance enhancement for a gps vector-tracking loop utilizing an adaptive iterated extended Kalman filter. Sensors 14(12), 23630–23649 (2014)CrossRefGoogle Scholar
  7. 7.
    Foremski, P., Gorawski, M., Grochla, K.: Energy-efficient crowdsensing of human mobility and signal levels in cellular networks. Sensors (2015)Google Scholar
  8. 8.
    Gorawski, M., Grochla, K.: Review of mobility models for performance evaluation of wireless networks. In: Gruca, A., Czachórski, T., Kozielski, S. (eds.) Man-Machine Interactions 3, AISC, vol. 242, pp. 567–577. Springer, Switzerland (2014)CrossRefGoogle Scholar
  9. 9.
    Grémillet, D., Dell’Omo, G., Ryan, P.G., Peters, G., Ropert-Coudert, Y., Weeks, S.J.: Offshore diplomacy, or how seabirds mitigate intra-specific competition: a case study based on GPS tracking of cape gannets from neighbouring colonies. Mar. Ecol. Prog. Ser. 268, 265–279 (2004)CrossRefGoogle Scholar
  10. 10.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35–45 (1960)Google Scholar
  11. 11.
    Marsland, S.: Machine Learning: An Algorithmic Perspective. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  12. 12.
    Mohamed, A., Schwarz, K.: Adaptive Kalman filtering for INS/GPS. J. geodesy 73(4), 193–203 (1999)CrossRefMATHGoogle Scholar
  13. 13.
    Pi, X., Mannucci, A., Lindqwister, U., Ho, C.: Monitoring of global ionospheric irregularities using the worldwide GPS network. Geophys. Res. Lett. 24(18), 2283–2286 (1997)CrossRefGoogle Scholar
  14. 14.
    Pilkington, N.: Kalman filter python implementation.
  15. 15.
    Rauh, A., Butt, S.S., Aschemann, H.: Nonlinear state observers and extended Kalman filters for battery systems. Int. J. Appl. Math. Comput. Sci. 23(3), 539–556 (2013)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Schofield, G., Bishop, C.M., MacLean, G., Brown, P., Baker, M., Katselidis, K.A., Dimopoulos, P., Pantis, J.D., Hays, G.C.: Novel GPS tracking of sea turtles as a tool for conservation management. J. Exp. Mar. Biol. Ecol. 347(1), 58–68 (2007)CrossRefGoogle Scholar
  17. 17.
    Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 87–119. APR, Springer, London (2001)CrossRefGoogle Scholar
  18. 18.
    Yuen, K.V., Hoi, K.I., Mok, K.M.: Selection of noise parameters for Kalman filter. Earthq. Eng. Eng. Vib. 6(1), 49–56 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Theoretical and Applied InformaticsPASGliwicePoland

Personalised recommendations