Advertisement

Optimal GNSS Signal Tracking Loop Design Based on Plant Modeling

  • Chongwon Kim
  • Sanghoon Jeon
  • Minhuck Park
  • Beomju Shin
  • Changdon KeeEmail author
Original Paper
  • 2 Downloads

Abstract

Conventional research for signal tracking of the Global Navigation Satellite System (GNSS) uses a loop filter to minimize the effect of measurement noise. Although for a few decades, research into the optimal GNSS tracking loop has been based on similarity between signal tracking and general control loop theory, it has mainly focused on optimal estimator, or shown vulnerability for high dynamic signal tracking. To enhance the performance of the optimal signal tracking loop, this paper proposes new plant modeling for optimal GNSS signal tracking that consists of both optimal estimator and controller. The proposed plant modeling is able to maximize the performance of the optimal signal tracking loop due to the relationships between code and carrier tracking, and between the frequency and phase of the carrier. In addition, the plant clearly defines a relationship between the general control loop and GNSS tracking loop, so that the plant is ready to be applied to various control theories for GNSS signal tracking. To assess the performance of the proposed plant modeling, we implement a linear quadratic Gaussian (LQG) tracking loop based on the new proposed plant and process simulation data. Comparison of the processing results to those of conventional research shows improved performance of the proposed plant modeling.

Keywords

GNSS Optimal signal tracking Plant modeling LQG Software receiver 

Notes

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (no. NRF-2017M1A3A3A02016230), contracted through the Institute of Advanced Aerospace Technology at Seoul National University. The Institute of Engineering Research at Seoul National University provided research facilities for this work.

References

  1. 1.
    Dierendonck AJV (1996) Chapter 8. GPS receiver. In: Parkinson BW, Enge P, Axelrad P, Spilker JJ Jr (eds) Global positioning system: theory and applications, vol 1. AIAA, WashingtonGoogle Scholar
  2. 2.
    Kaplan E, Hegarty C (2005) Chapter 5. Satellite signal, acquisition, tracking, and data demodulation. In: Kaplan E, Hegarty C (eds) Understanding GPS: principles and applications, 2nd edn. Artech House, Norwood, MAGoogle Scholar
  3. 3.
    Fikes MP (1994) GPS receiver tracking loop optimization using L1 theory. Master, Aeronautics and Astronautics, MITGoogle Scholar
  4. 4.
    Iltis RA, Hanson GA (1999) C/A code tracking and acquisition with interference rejection using the extended Kalman filter. In: Institute of Navigation, National Technical Meeting ‘Vision 2010: Present and Future’, San Diego, CAGoogle Scholar
  5. 5.
    Jee G-I, Kim H, Lee Y, Park C (2002) A GPS C/A code tracking loop based on extended Kalman filter with multipath mitigation. In: Proceedings of the 15th international technical meeting of the satellite division of the Institute of Navigation (ION GPS 2002)Google Scholar
  6. 6.
    Spangenberg M, Heiries V, Giremus A, Calmettes V (2005) Multi-channel extended Kalman filter for tracking BOC modulated signals in the presence of multipath. In: Proceedings of the 18th international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2005)Google Scholar
  7. 7.
    Ziedan NI (2011) Multi-frequency combined processing for direct and multipath signals tracking based on particle filtering. In: Proceedings of the 24th international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2011)Google Scholar
  8. 8.
    Psiaki ML, Jung H (2002) Extended Kalman filter methods for tracking weak GPS signals. In: Proceedings of the 15th international technical meeting of the satellite division of the Institute of Navigation (ION GPS 2002)Google Scholar
  9. 9.
    Ziedan NI, Garrison JL (2004) Extended Kalman filter-based tracking of weak GPS signals under high dynamic conditions. In: Proceedings of the 17th international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2004)Google Scholar
  10. 10.
    Ziedan NI (2011) Bayesian filtering approaches for unambiguous BOC tracking under weak signal conditions. In: Proceedings of the ION GNSS 2011. Portland, ORGoogle Scholar
  11. 11.
    Chen Y-H, Juang J-C, Kao T-L (2010) Robust GNSS signal tracking against scintillation effects: a particle filter based software receiver approach. In: Proceedings of the 2010 international technical meeting of the Institute of NavigationGoogle Scholar
  12. 12.
    Zhang L, Morton YT (2010) A variable gain adaptive Kalman filter-based GPS carrier tracking algorithm for ionosphere scintillation signals. In: Proceedings of the 23rd international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2010)Google Scholar
  13. 13.
    Barreau V, Vigneau W, Macabiau C, Deambrogio L (2012) Kalman Filter based robust GNSS signal tracking algorithm in presence of ionospheric scintillations. In: Satellite navigation technologies and European workshop on GNSS signals and signal processing (NAVITEC), 2012 6th ESA workshop on, 2012Google Scholar
  14. 14.
    Macabiau C, Deambrogio L, Barreau V, Vigneau W, Valette J-J, Artaud G et al (2012) Kalman filter based robust GNSS signal tracking algorithm in presence of ionospheric scintillations. In: ION GNSS 2012, 25th international technical meeting of the satellite division of the Institute of NavigationGoogle Scholar
  15. 15.
    Ferrario A, Zina A, Siniscalco L, Emmanuele A, Pastori N, Crosta P (2015) Improvement of a high-grade GNSS receiver robustness against ionospheric Scintillations using a Kalman filter tracking scheme. In: Proceedings of the 28th international technical meeting of the satellite division of the Institute of Navigation (ION GNSS+ 2015)Google Scholar
  16. 16.
    Jee G-I, Im S-H, Lee B-H (2007) Optimal code and carrier tracking loop design for Galileo BOC (1, 1). In: Proceedings of the 20th international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2007), Fort Worth, TXGoogle Scholar
  17. 17.
    Im S-H, Song J-H, Jee G-I, Park CG (2008) Comparison of GPS tracking loop performance in high dynamic condition with nonlinear filtering techniques. In: Proceedings of the 21st international technical meeting of the satellite division of the Institute of Navigation (ION GNSS 2008)Google Scholar
  18. 18.
    Jeon S, Kim C, Kim G, Kim O, Kee C (2013) Optimal signal tracking algorithm for GNSS signal using moving set-point LQG system. Int J Control Autom Syst 11(6):1214–1222CrossRefGoogle Scholar
  19. 19.
    Hespanha JP (2007) LQG/LQR controller design. Undergraduate Lecture Notes. University of California, Santa Barbara, California, USAGoogle Scholar
  20. 20.
    Anderson BDO, Moore JB (1989) Optimal control, linear quadratic methods. Prentice-Hall International Inc., CanberraGoogle Scholar
  21. 21.
    Jung H, Psiaki ML, Powell SP (2003) Kalman-filter-based semi-codeless tracking of weak dual-frequency GPS signals. In: Proceedings of 16th international technical meeting of satellite division of the Institute of Navigation (ION NTM2003)Google Scholar
  22. 22.
    Brown RG, Hwang PYC (1992) Introduction to random signals and applied Kalman filtering, 3rd edn. Wiley, New YorkzbMATHGoogle Scholar
  23. 23.
    Hespanha JP (2005) Lecture notes on LQR/LQG controller design. Knowledge creation diffusion utilizationGoogle Scholar
  24. 24.
    Franklin GF, Powell JD, Emami-Naeini A (2002) Feedback control of dynamic systems, vol 4. Prentice Hall, Upper SaddlezbMATHGoogle Scholar
  25. 25.
    STR4500 GPS (2005) SBAS simulator with SimPLEX software user manual. Spirent Communications Limited, Bangalore, IndiaGoogle Scholar
  26. 26.
    Kim G, So H, Jeon S, Kee C, Cho Y, Choi W (2008) The development of modularized post processing GPS software receiving platform using MATLAB simulink. Int J Aeronaut Space Sci 9(2):121–128CrossRefGoogle Scholar

Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  • Chongwon Kim
    • 1
    • 3
  • Sanghoon Jeon
    • 2
    • 3
  • Minhuck Park
    • 1
  • Beomju Shin
    • 1
  • Changdon Kee
    • 1
    Email author
  1. 1.Department of Aerospace EngineeringSeoul National UniversitySeoulSouth Korea
  2. 2.BK21Plus Transformative Training Program for Creative Mechanical and Aerospace EngineersSeoul National UniversitySeoulSouth Korea
  3. 3.Kakao MobilitySeongnam-siSouth Korea

Personalised recommendations