Skip to main content
SpringerLink
Log in

Key Scientific Problems Based on GPS Positioning System

  • Chapter
  • First Online:
Book cover Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway

  • 270 Accesses

Abstract

In recent years, China’s rail transit industry has entered a period of rapid development. The national “12th five-year plan for comprehensive transportation system” has clearly defined the construction plan and high-speed railway construction plan of China’s central and Western regions in the future period and also put forward new requirements for railway transportation technology and safety equipment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ning B, Tang T, Li KC, Dong HR. High-speed train control system. Beijing: Science Press; 2012.

    Google Scholar 

  2. Dong H, Ning B, Cai B, et al. Automatic train control system development and simulation for high-speed railways. IEEE Circuits Syst Mag. 2010;10(2):6–18.

    Article  Google Scholar 

  3. Zhu AH, Li B, Yang L. Research on positioning technology and combined positioning system for high-speed train. Chin Railw. 2013;5:59–63.

    Google Scholar 

  4. Chen DW, Gao Q, Pei LJ. Study on digital track map based on graph theory and oriented object. J China Railw Soc. 2012;34(6):64–8.

    Google Scholar 

  5. Liu J, Cai BG, Tang T, et al. Fault-tolerant algorithm design of low-cost integrated train positioning system. J China Railw Soc. 2011;33(1):39–46.

    Google Scholar 

  6. Guo BQ, Tang T, Zhou DT. Map aided positioning method used in train locating application. J China Railw Soc. 2007;29(6):44–7.

    Google Scholar 

  7. Liu J, Cai BG, Tang T, et al. Train integrated positioning system based on GPS and inertial measurement unit. China Railw Sci. 2010;31(1):123–9.

    Google Scholar 

  8. Kang SW, Liu XJ. Research on train location method based on multi-sensor information fusion. Railw Comput Appl. 2014;23(1):14–7.

    Google Scholar 

  9. China’s Ministry of Railways. Railway survey regulations of global positioning system (TB10054-97). Beijing: China Railway Press; 1997.

    Google Scholar 

  10. Tang JG. Analysis for train control system of Qinghai-Tibet railway based on GPS and GSM-R. J Transp Syst Eng Inf Technol. 2010;10(2):190–4.

    Google Scholar 

  11. Cheng R, Song Y, Chen D, Cheng L. Intelligent localization of high-speed train using LSSVM and online sparse optimization approach. IEEE Trans Intell Transp Syst. 2017;18:2071–84.

    Article  Google Scholar 

  12. Chen D, Han X, Cheng R, Yang L. Position calculation models by neural computing and online learning methods for high-speed train. Neural Comput Appl. 2016;27:1617–28.

    Article  Google Scholar 

  13. Chen D, Wang L, Li L. Position computation models for high-speed train based on support vector machine approach. Appl Soft Comput. 2015;30:758–66.

    Article  Google Scholar 

  14. Suykens JAK, De Brabanter J, Lukas L, et al. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomput. 2002;48(1–4):85–105.

    Article  Google Scholar 

  15. de Kruif BJ, de Vries TJA. Pruning error minimization in least squares support vector machines. IEEE Trans Neural Netw. 2003;14(3):696–702.

    Article  Google Scholar 

  16. Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern). 1996;26:29–41.

    Article  Google Scholar 

  17. Cheng R, Song Y, Chen D, Ma X. Intelligent positioning approach for high speed trains based on ant colony optimization and machine learning algorithms. IEEE Trans Intel Transp Syst. 2018. To be published.

    Google Scholar 

  18. Jia XZ, Chen DW. Study on information fusion algorithm for multiple GPS railway track data. J China Railw Soc. 2011;33(9):72–5.

    Google Scholar 

  19. Chen DW, Cai BG, Wang J, Tang T. Mathematical model and algorithm of data reduction for railway GPS data. J China Railw Soc. 2008;30(4):116–9.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fuzhou University, Fuzhou, China

    Dewang Chen

  2. SKL of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China

    Ruijun Cheng

Authors
  1. Dewang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruijun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dewang Chen .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Beijing Jiaotong University Press and Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, D., Cheng, R. (2019). Key Scientific Problems Based on GPS Positioning System. In: Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58970-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-58970-0_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-58968-7

  • Online ISBN: 978-3-662-58970-0

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation