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Integrated GNSS QoS Prediction for Navigation Services

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Published:05 November 2013Publication History

ABSTRACT

As users increasingly rely on navigation assistance, they expect accurate and reliable navigation performances, especially in obstructed areas where positioning quality of global navigation satellite system (GNSS) is often deteriorated. This paper presents a novel methodology, called integrated GNSS (iGNSS) quality of service (QoS) prediction to provide a means for navigation applications to plan according to GNSS positioning quality. The methodology could be used to predict iGNSS QoS on prospective route segments ahead of time, among other tasks, allowing the navigation service to prepare a suitable plan before the users reaches the poor iGNSS QoS segments. The results were evaluated by comparing the predicted iGNSS QoS to the collected GPS data on sample routes in different environment surroundings under various parameter settings. The results show that the proposed methodology is capable of predicting GNSS QoS on segments under open sky condition accurately and identifying locations on segments with poor GNSS QoS.

References

  1. Karimi, H. Roongpiboonsopit, D., and Kasemsuppakorn, P. 2011. Uncertainty in personal navigation services, J. Nav. 64, 2 (Mar. 2011), 341--356.Google ScholarGoogle Scholar
  2. Seynat, C., Kealy, A. and Zhang, K. 2004. A performance analysis of future Global Navigation Satellite Systems. J. GPS. 3, 232--241.Google ScholarGoogle ScholarCross RefCross Ref
  3. Karimi, H., Liu, X., Liu, S. and Hammad, A. 2004. GPSLoc: framework for predicting Global Positioning System quality of service. J. Comput. Civil Eng.18, 196--206.Google ScholarGoogle ScholarCross RefCross Ref
  4. Germroth, M. and Carstensen, L. 2005. GIS and satellite visibility: viewsheds from space. In Proceeding of the ESRI International User Conference 2005 (San Diego, California, July 25--29, 2005).Google ScholarGoogle Scholar
  5. Lee, Y., Suh, Y. and Shibasaki, R. 2008. A simulation system for GNSS multipath mitigation using spatial statistical methods. Comput. Geosci. 34, 1597--1609. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Marais, J., Berbineau, M. and Heddebaut, M. 2005. Land mobile GNSS availability and multipath evaluation tool. IEEE Trans. Veh. Technol. 54, 1697--1704.Google ScholarGoogle ScholarCross RefCross Ref
  7. Verhagen, S. 2002. Studying the performance of Global Navigation Satellite Systems: a new software tool. GPS World. 01 2012.Google ScholarGoogle Scholar
  8. Taylor, G., Li, J., Kidner, D., Brunsdon, C. and Ware, M. 2007. Modelling and prediction of GPS availability with digital photogrammetry and LiDAR. Int. J. GIS. 21, 1--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Lee, Y., Suh, Y. and Shibasaki, R. 2008. A GIS-based simulation to predict GPS availability along the Tehran Road in Seoul, Korea. KSCE J. Civ. Eng. 12, Nov, 401--408.Google ScholarGoogle ScholarCross RefCross Ref
  10. Karimi, H., Zimmerman, B., Roongpiboonsopit, D., and Rezgui, A. 2011. Grid based geoprocessing for integrated global navigation satellite system simulation. J. Comput. Civil Eng. 26, 1, 19--27.Google ScholarGoogle ScholarCross RefCross Ref
  11. Karimi, H., Roongpiboonsopit, D. and Wang, H. 2011. Exploring real-time geoprocessing in cloud computing: navigation services case study. Trans. in GIS. 15, 5, 613--633.Google ScholarGoogle ScholarCross RefCross Ref
  12. Roongpiboonsopit, D. and Karimi, H.. 2012. Integrated Global Navigation Satellite System (iGNSS) QoS prediction. Photogramm. Eng. & Remote Sens. 78, 2, 139--149.Google ScholarGoogle ScholarCross RefCross Ref
  13. Kasemsuppakorn, P. and Karimi, H. 2009. Personalised routing for wheelchair navigation. J. Loc. Based Serv. 3, 1. 24--5 Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        IWCTS '13: Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
        November 2013
        92 pages
        ISBN:9781450325271
        DOI:10.1145/2533828

        Copyright © 2013 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 5 November 2013

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        Acceptance Rates

        IWCTS '13 Paper Acceptance Rate14of22submissions,64%Overall Acceptance Rate42of57submissions,74%

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