Circular particle fusion filter applied to map matching

Circular particle fusion filter applied to map matching

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Navigation in constrained areas such as ports or dense urban environments is often exposed to global navigation satellite system (GNSS) satellites masking caused by the infrastructures. In this case, the GNSS positioning is inaccurate or unavailable and proprioceptive sensors are generally used to temporarily localise the vehicle on a map. However, the drift of these sensors rapidly causes the navigation system to fail. In this study, the navigation is computed using magnetometer and GNSS observations defined in an absolute reference frame. The heading measurements are coupled with a digital map of the road network in order to localise the vehicle in a map matching process. The contribution of this study is the proposition of a particle filter that fuses the position and direction observations to estimate the vehicle position. In this context, when the GNSS signal is masked, the observations of direction are used to compute the vehicle position. The proposed filter is defined in both circular and linear domains in order to take into account the nature of the observations. The proposed approach is assessed on real and synthetic data.


    1. 1)
      • 1. Jimenez, F., Monzon, S., Naranjo, J.E.: ‘Definition of an enhanced map-matching algorithm for urban environments with poor GNSS signal quality’, Sensors, 2016, 16, p. 193.
    2. 2)
      • 2. Lianxia, X., Quan, L., Minghua, L., et al: ‘Map matching algorithm and its application’. Proc. of the Int. Conf. on Intelligent Systems and Knowledge Engineering (ISKE), 2007.
    3. 3)
      • 3. Wang, W., Jin, J., Ran, B., et al: ‘Integrated map matching algorithm for GPS-based freeway network traffic monitoring’. Transportation Research Board 89th Annual Meeting, 2010.
    4. 4)
      • 4. Quddus, M., Washington, S.: ‘Shortest path and vehicle trajectory aided map-matching for low frequency GPS data’, Transp. Res. C, 2015, 55, pp. 328339.
    5. 5)
      • 5. Quddus, M.A.: ‘High integrity map matching algorithms for advanced transport telematics applications’. Ph.D. dissertation, Imperial College, London, UK, 2006.
    6. 6)
      • 6. Bierlaire, M., Chen, J., Newman, J.: ‘A probabilistic map matching method for smartphone GPS data’, Transp. Res. C, 2013, 26, pp. 7898.
    7. 7)
      • 7. Ren, M., Karimi, H.A.: ‘A fuzzy logic map matching for wheelchair navigation’, GPS Solut., 2012, 16, (3), pp. 273282, doi: 10.1007/s10291-011-0229-5.
    8. 8)
      • 8. Nassreddine, G., Abdallah, F., Denoeux, T.: ‘Map matching algorithm using belief function theory’. Proc. of the 11th Int. Conf. on Information Fusion (FUSION ‘08), 2008, pp. 9951002.
    9. 9)
      • 9. Toledo-Moreo, R., Betaille, D., Peyret, F., et al: ‘Fusing GNSS, dead-reckoning, and enhanced maps for road vehicle lane-level navigation’, IEEE J. Sel. Top. Signal Process., 2009, 3, (5), pp. 798809.
    10. 10)
      • 10. Parent, C., Spaccapietra, S., Renso, C., et al: ‘Semantic trajectories modeling and analysis’, ACM Comput. Surv., 2012, 45, (4), pp. 42:142:32.
    11. 11)
      • 11. Quddus, M.A., Ochieng, W.Y., Noland, R.B.: ‘Current map-matching algorithms for transport applications: state-of-the art and future research directions’, Transp. Res. C: Emerg. Technol., 2007, 15, (5), pp. 312328.
    12. 12)
      • 12. Quddus, M.A., Ochieng, W.Y., Zhao, L., et al: ‘A general map matching algorithm for transport telematics applications’, GPS Solut., 2003, 7, (3), pp. 157167.
    13. 13)
      • 13. Velaga, N.R., Quddus, M.A., Bristow, A.L.: ‘Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems’, Transp. Res. C: Emerg. Technol., 2009, 17, (6), pp. 672683.
    14. 14)
      • 14. Haiqiang, Y., Shaowu, C., Huifu, J., et al: ‘An enhanced weight-based topological map matching algorithm for intricate urban road network’, Procedia – Soc. Behav. Sci., 2013, 96, pp. 16701678.
    15. 15)
      • 15. Hashemi, M., Karimi, H.A.: ‘A critical review of real-time map-matching algorithms: Current issues and future directions’, Comput. Environ. Urban Syst., 2014, 48, pp. 153165.
    16. 16)
      • 16. Smaili, C., El Badaoui El Najjar, M., Charpillet, F.: ‘A hybrid Bayesian framework for map matching: Formulation using switching Kalman filter’, J. Intell. Robot Syst., 2014, 74, pp. 725743.
    17. 17)
      • 17. Wu, Q., Gu, X., Luo, J., et al: ‘A vehicle map-matching algorithm based on measure fuzzy sorting’, J. Comput., 2014, 9, pp. 10581065.
    18. 18)
      • 18. Cossaboom, M., Georgy, J., Karamat, T., et al: ‘Augmented Kalman filter and map matching for 3D RISS/GPS integration for land vehicles’, Int. J. Navig. Observ., 2012, 2012, pp. 116.
    19. 19)
      • 19. Gustafsson, F., Gunnarsson, F., Bergman, N., et al: ‘Particle filters for positioning, navigation and tracking’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 425437.
    20. 20)
      • 20. Caron, F., Davy, M., Duflos, E., et al: ‘Particle filtering for multisensor data fusion with switching observation models: application to land vehicle positioning’, IEEE Trans. Signal Process., 2007, 55, (6), pp. 27032719.
    21. 21)
      • 21. Giremus, A., Tourneret, J.Y., Calmettes, V.: ‘A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements’, IEEE Trans. Signal Process., 2007, 55, (4), pp. 12751285.
    22. 22)
      • 22. Rabaoui, A., Viandier, N., Duflos, E., et al: ‘Dirichlet process mixtures for density estimation in dynamic nonlinear modeling: application to GPS positioning in urban canyons’, IEEE Trans. Signal Process., 2012, 60, (4), pp. 16381655.
    23. 23)
      • 23. Kuhnt, F., Kohlhaas, R., Jordan, R., et al: ‘Particle filter map matching and trajectory prediction using a spline based intersection model’. 17th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Qingdao, 2014, pp. 18921893.
    24. 24)
      • 24. Fouque, C., Bonnifait, P.: ‘Matching raw GPS measurements on a navigablemap without computing a global position’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 887898.
    25. 25)
      • 25. Davidson, P., Collin, J., Takala, J.: ‘Application of particle filters to a map-matching algorithm’, Gyroscopy Navig., 2011, 2, (4), p. 285.
    26. 26)
      • 26. Georgy, J., Noureldin, A., Goodall, C.: ‘Vehicle navigator using a mixture particle filter for inertial sensors/odometer/map data/GPS integration’, IEEE Trans. Consum. Electron., 2012, 58, (2), pp. 544552.
    27. 27)
      • 27. Jammalamadaka, S.R., SenGupta, A.: ‘Topics in circular statistics’ (World Scientific Publ., New Jersey, 2001).
    28. 28)
      • 28. Arulampalam, S., Maskell, S., Gordon, N., et al: ‘A tutorial on particle filters for online nonlinear/Non-Gaussian Bayesian tracking’, IEEE Trans. Signal Process., 2002, 50, pp. 174188.
    29. 29)
      • 29. OpenStreetMap website. Available at

Related content

This is a required field
Please enter a valid email address