Advertisement

The Research of Dynamic Tracking Algorithm Based on Hybrid Positioning System

  • Chengbiao Fu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

In signal-degraded environments such as dense urban area and indoor environment, GPS (Global Positioning System) signals are either blocked or strongly degraded by natural and artificial obstacles, which cannot meet the surging demands for position information, the combination of different GNSS (Global Navigation Satellite System) could be a suitable approach to fill this gap. This paper presents a hybrid positioning method combining GPS and GLONASS, simulation results show that Extended Kalman Filter Algorithm is an effective method to deal with data fusion in hybrid positioning system, so this system can improve the positioning accuracy in the environment without enough GPS satellites.

Keywords

GPS GLONASS Integrated navigation Extended Kalman filter 

References

  1. 1.
    Hideki Y, Tomoji T, Nobuaki K, Akio Y (2008) Evaluation of positioning accuracy with differential GPS/GLONASS. 21st international technical meeting of the Satellite Division of the Institute of Navigation, ION GNSS, vol 2. pp 669–674
  2. 2.
    Juju D, Yunzhong S (2012) An algorithm of combined GPS/GLONASS static relative positioning. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 41(6):825–830, 917Google Scholar
  3. 3.
    Xiangguang M, Jiming G (2010) GPS-GLONASS and their combined precise point positioning. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics Inform Sci Wuhan Univ 35(12):1409–1413Google Scholar
  4. 4.
    Changsheng C, Yang G (2013) Modeling and assessment of combined GPS/GLONASS precise point positioning. GPS Solutions 17(2):223–236, doi:  10.1007/s10291-012-0273-9 CrossRefGoogle Scholar
  5. 5.
    Xiaohong Z, Fei G, Xingxing L, Xiaojing L (2010) Study on precise point positioning based on combined GPS and GLONASS. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics Inform Sci Wuhan Univ 35(1):9–12Google Scholar
  6. 6.
    Changsheng C, Yang G (2013) GLONASS-based precise point positioning and performance analysis. Adv Space Res 51(3):514–524. doi: doi: 10.1016/j.asr.2012.08.004 CrossRefGoogle Scholar
  7. 7.
    Yang Xu, Xu Qing, Wang Haijiang (2012) An Kalman filter-based method for BeiDou/GPS integrated navigation system. The 2012 Proceedings of the international conference on communications, signal processing, and systems, lecture notes in electrical engineering, vol 202. pp 485–492 doi:  10.1007/978-1-4614-5803-6_49
  8. 8.
    Tian Zengshan, Lei Luo (2008) Particle filter positioning and tracking based on dynamic model. 4th IEEE conference on automation science and engineering, CASE, pp 756–759. doi:  10.1109/COASE.2008.4626430

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringQujing Normal CollegeQujingChina

Personalised recommendations