DOI QR코드

DOI QR Code

Reduction of GPS Latency Using RTK GPS/GNSS Correction and Map Matching in a Car NavigationSystem

  • Kim, Hyo Joong (Energy & Chemical Business Team II, SK Holdings C&C) ;
  • Lee, Won Hee (School of Convergence & Fusion System Engineering, Kyungpook National University) ;
  • Yu, Ki Yun (Dept. of Civil & Environmental Engineering, Seoul National University)
  • 투고 : 2016.05.17
  • 심사 : 2016.06.24
  • 발행 : 2016.06.30

초록

The difference between definition time of GPS (Global Positioning System) position data and actual display time of car positions on a map could reduce the accuracy of car positions displayed in PND (Portable Navigation Device)-type CNS (Car Navigation System). Due to the time difference, the position of the car displayed on the map is not its current position, so an improved method to fix these problems is required. It is expected that a method that uses predicted future positionsto compensate for the delay caused by processing and display of the received GPS signals could mitigate these problems. Therefore, in this study an analysis was conducted to correct late processing problems of map positions by mapmatching using a Kalman filter with only GPS position data and a RRF (Road Reduction Filter) technique in a light-weight CNS. The effects on routing services are examined by analyzing differences that are decomposed into along and across the road elements relative to the direction of advancing car. The results indicate that it is possible to improve the positional accuracy in the along-the-road direction of a light-weight CNS device that uses only GPS position data, by applying a Kalman filter and RRF.

키워드

참고문헌

  1. Ahn, D. and Lee, D., 2005, Performance Improvement of Map Matching Using Compensation Vectors, The Transactions of the Korean Institute of Electrical Engineers, Vol. 54, No. 2, pp. 97-103.
  2. Bernstein, D. and Kornhauser, A., 1996, An introduction to map matching for personal navigation assistants, Technical report, New Jersey TIDE Center Technical Report.
  3. Bonnifait, P., Bouron, P., Crubille, P. and Meizel D., 2001, Data Fusion of Four ABS Sensors and GPS for an Enhanced Localization of Car-like Vehicles, Proc. of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, pp. 1597-1602.
  4. Friedland, B., 1973, Optimal Steady-State Positions and Velocity Estimation Using Noisy. Sampled Position Data, IEEE Transactions on Aerospace and Electronic Systems, Vol. 9, No. 6, pp. 906-911.
  5. Kohler, M., 1997, Using the Kalman Filter to Track Human. Interactive Motion-Modelling and Initialization of the Kalman Filter for Translational Motion, Technical report, Dortmund University.
  6. Ga, C. O, Lee, W. H. and Yu, K. Y., 2011, Study on the Method to Create a Pedestrian Network and Path using Navigation Data for Vehicles, Journal of the Korean Society for GeoSpatial Information Science Vol. 19, No. 3, pp. 64-74.
  7. Gortcheva, A., Garrido, R., Gonzalez and E., Carvallo, A., 2001, Predicting a moving object position for visual servoing: Theory and experiments, International Journal of Adaptive Control and Signal Processing, Vol. 15, No. 4, pp. 377-392. https://doi.org/10.1002/acs.628
  8. Li, J., Taylor, G. E. and Kidner, D. B., 2005, Accuracy and reliability of map matched GPS coordinates: dependence on terrain model resolution and interpolation algorithm, Computers and Geosciences, Vol. 31, No. 2, pp. 241-251. https://doi.org/10.1016/j.cageo.2004.06.011
  9. Manfredi, V., Mahadevan, S. and Kurose, J., 2005, Switching Kalman Filters for Prediction and Tracking in an Adaptive Meteorological Sensing Network, Proc. of IEEE Conference on Sensor and Ad Hoc Communications and Networks, Santa Clara, California, USA, pp. 197-206.
  10. Quddus, M. A., Ochieng, W. Y., Zhao, L. and Noland, R. B., 2003, A general map-matching algorithm for transport telematics applications, GPS Solutions, Vol. 7, No. 3, pp. 157-167. https://doi.org/10.1007/s10291-003-0069-z
  11. Quddus, M. A., Noland, R. B. and Ochieng, W. Y., 2005, Validation of map matching algorithm using high precision positioning with GPS, Journal of Navigation - The Royal Institute of Navigation, Vol. 58, No. 2, pp. 257-271. https://doi.org/10.1017/S0373463305003231
  12. Singer, R. A., 1970, Estimation Optimal Tracking Filter Performance for Manned Maneuvering Targets, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-6, No. 4, pp. 473-483. https://doi.org/10.1109/TAES.1970.310128
  13. Takenga, C., Peng, T. and Kyamakya, K., 2007, Post-Processing of Fingerprint Localization using Kalman Filter and Map-matching Techniques, Proc. of the 9th IEEE international conference on advanced communication technology, Seoul, Korea, pp. 2029-2034.
  14. Taylor, G., Blewitt, G., Steup, D., Corbett, S. and Car, A., 2001, Road reduction filtering for GPS-GIS navigation, Transactions in GIS, Vol. 5, No. 3, pp. 193-207. https://doi.org/10.1111/1467-9671.00077
  15. Taylor, G. and Blewitt, G, 2006, Intelligent Positioning GIS-GPS Unification, John Wiley & Sons, Ltd.
  16. Tessier, C., Cariou, C., Debain, C., Chausse, F., Chapuis, R. and Rousset, C., 2006, A real-time, multi-sensor architecture for fusion of delayed observations: application to vehicle localization, Proc. of the 9th IEEE International Conference on Intelligent Transportation Systems, Toronto, Ontario, Canada, pp. 1316-1321.
  17. Tradisauskas, N., Juhl, J., Lahrmann, H. and Jensen, C. S., 2009, Map matching for intelligent speed adaption, IET Intelligent Transport Systems, Vol. 3, No. 1, pp. 57-66. https://doi.org/10.1049/iet-its:20070036
  18. Trimble, 2008, Trimble R8 GNSS ROVER Datasheet.
  19. Xu, H., Liu, H, Norville, H. S. and Bao, Y., 2007, A virtual differential map-matching algorithm, Proc. of the 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA, pp. 448-453.
  20. Yick, J., Mukherjee, B. and Ghosal, D., 2005, Analysis of a prediction-based adaptive mobility tracking algorithm, Proc. of the 2nd International Conference on Broadband Networks, London, UK, pp. 753-760.

피인용 문헌

  1. RTK Latency Estimation and Compensation Method for Vehicle Navigation System vol.6, pp.1, 2017, https://doi.org/10.11003/jpnt.2017.6.1.17