Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-20T06:38:21.285Z Has data issue: false hasContentIssue false
  • English
  • Français

An Adaptive Vector Tracking Scheme for High-Orbit Degraded GNSS Signal

Published online by Cambridge University Press:  17 November 2020

Chenyang Jiao ,
Xinlong Wang ,
Dun Wang ,
Qunsheng Li ,
Jinpeng Zhang  and
Yuanwen Cai
Chenyang Jiao
Affiliation:
(School of Astronautics, Beihang University, China)
Xinlong Wang*
Affiliation:
(School of Astronautics, Beihang University, China)
Dun Wang
Affiliation:
(State Key Laboratory of Space-Ground Information Technology, Beijing, China)
Qunsheng Li
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, China)
Jinpeng Zhang
Affiliation:
(Luoyang Optoelectro Technology Development Center, Luoyang, China)
Yuanwen Cai
Affiliation:
(Department of Graduate School, Space Engineering University, Beijing, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

Keywords

High-Orbit EnvironmentVector Tracking LoopVariational Bayesian AlgorithmSignal OutageTime-Varying Noise
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 105 - 124
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Axelrad, P., Bradley, B. K., Tombasco, J., Mohiuddin, S. and Donna, J. (2010). GEO Satellite Positioning Using GPS Collective Detection. Proc. ION GNSS 2010, Institute of Navigation, Portland, Oregon, USA, September 21–24, pp. 2706–2716.Google Scholar
Beal, M.J. (2003). Variational algorithms for approximate Bayesian inference. PhD, The Gatsby Computational Neuroscience Unit, University of London.Google Scholar
Bhatt, D., Aggarwal, P., Devabhaktuni, V. and Bhattacharya, P. (2014). A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS. Expert Systems with Applications, 41(5), 2166–2173.CrossRefGoogle Scholar
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.Google Scholar
Capuano, V., Botteron, C. and Farine, P. A. (2013). GNSS Performances for MEO, GEO and HEO. International Astronautical Congress, Beijing, China, October 23-27, pp. 1–15.Google Scholar
Chen, X., Wang, X. and Xu, Y. (2014). Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter. Sensors, 14(12), 23630–23649.CrossRefGoogle ScholarPubMed
Dardin, S. F. S., Calmettes, V., Priot, B. and Toumeret, J. Y. (2013). Design of an Adaptive Vector-Tracking Loop for Reliable Positioning in Harsh Environment. Proc. ION GNSS 2013, Institute of Navigation, Nashville, Tennessee, USA, September 16–20, pp. 3548–3559.Google Scholar
Falletti, E., Pini, M. and Lo Presti, L. (2011). Low complexity carrier-to-noise ratio estimators for GNSS digital receivers. IEEE Transactions on Aerospace and Electron Systems, 47, 420–437.CrossRefGoogle Scholar
Frangos, K., Kealy, A., Gikas, V. and Hasnur, A. (2010). Dynamic Modeling for Land Mobile Navigation Using Low-Cost Inertial Sensors and Least Squares Support Vector Machine Learning. Proc. ION GNSS 2010, Institute of Navigation, Portland, Oregon, USA, September 21–24, pp. 1687–1696.Google Scholar
Garcia, E. and Hausotte, T. (2013). The parallel Bayesian Toolbox for high-performance Bayesian filtering in metrology. Measurement Science Review, 13(6), 315–321.CrossRefGoogle Scholar
Gustafsson, F. and Hendeby, G. (2012). Some relations between extended and unscented Kalman filters. IEEE Transactions on Signal Processing, 60(2), 545–555.CrossRefGoogle Scholar
Hsu, L., Jan, S., Groves, P.D. and Kubo, N. (2015). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions, 19, 249–262.CrossRefGoogle Scholar
Jwo, D. J., Wen, Z. M. and Lee, Y. C. (2015). Vector tracking loop assisted by the neural network for GPS signal blockage. Applied Mathematical Modelling, 39(19), 5949–5968.CrossRefGoogle Scholar
Kanwal, N., Hurskainen, H. and Nurmi, J. (2010). Vector Tracking Loop Design for Degraded Signal Environment. Ubiquitous Positioning Indoor Navigation and Location Based Service, Helsinki, Finland, Oct. 14–15, pp. 1–4, doi:10.1109/UPINLBS.2010.5654015.CrossRefGoogle Scholar
Kim, K. H., Jee, G. I. and Im, S. H. (2011). Adaptive vector-tracking loop for low-quality GPS signals. International Journal of Control Automation & Systems, 9(4), 709–715.CrossRefGoogle Scholar
Kurban, T. and Beždok, E. (2009). A comparison of RBF neural network training algorithms for inertial sensor based terrain classification. Sensors, 9(8), 6312–6329.CrossRefGoogle ScholarPubMed
Lashley, M., Bevly, D. M. and Hung, J. Y. (2009). Performance analysis of vector tracking algorithms for weak GPS signals in high dynamics. IEEE Journal of Selected Topics in Signal Processing, 3(4), 661–673.CrossRefGoogle Scholar
Li, Z. K., Wang, J., Li, B. H., Gao, J. X. and Tan, X. L. (2014). GPS/INS/Odometer integrated system using fuzzy neural network for land vehicle navigation applications. Journal of Navigation, 67(6), 967–983.CrossRefGoogle Scholar
Li, G., Wu, J., Zhao, C. and Tian, Y. (2017). Double differencing within GNSS constellations. GPS Solutions, 21(3), 1161–1177.CrossRefGoogle Scholar
Lorga, J. F. M., Silva, P. F., Dovis, F., Cintio, A. D., Kowaltschek, S., Jimenez, D. and Jansson, R. (2011). Autonomous Orbit Determination for Future GEO and HEO Missions. Satellite Navigation Technologies and European Workshop on Gnss Signals and Signal Processing, IEEE, Noordwijk, The Netherlands, December 8–10, pp. 1–14.Google Scholar
Mbalawata, I. S., Vihola, M. and Haario, H. (2015). Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter. Computational Statistics & Data Analysis, 83(C), 101–115.CrossRefGoogle Scholar
Moreau, M. C., Axelrad, P., James, L., Garrison, J. L. and Long, A. (2000). GPS receiver architecture and expected performance for autonomous navigation in high earth orbits. Journal of Navigation, 47(3), 191–204.Google Scholar
Moreau, M. C., Bauer, F. H., Carpenter, J. R., Davis, E. P., Davis, G. W. and Jackson, L. A. (2002). Results from the GPS Flight Experiment on the High Earth Orbit AMSAT OSCAR-40 Spacecraft. Proc. INO ITM 2002, Institute of Navigation, Breckenridge, CO, USA, February 6–10, pp. 45–59.Google Scholar
Särkkä, S. (2013). Bayesian filtering and smoothing. New York: Cambridge University Press.CrossRefGoogle Scholar
Särkkä, S. and Nummenmaa, A. (2009). Recursive noise adaptive Kalman filtering by variational Bayesian approximations. IEEE Transactions on Automatic Control, 54(3), 596–600.CrossRefGoogle Scholar
Sato, M. A. (2001). Online model selection based on the variational Bayes. Neural Computation, 13(7), 1649–1681.CrossRefGoogle Scholar
Schaffrin, B. and Kwon, J. H. (2002). A Bayes filter in Friedland form for INS/GPS vector gravimetry. Geophysical Journal of the Royal Astronomical Society, 149(1), 64–75.CrossRefGoogle Scholar
Shen, N. and Zhang, X. (2013). The design for high dynamic GPS receiver in a combinated method of FLL and PLL. In Wong, W. E. and Ma, T. (eds.), Emerging Technologies for Information Systems, Computing, and Management, Vol. 236. New York: Springer, pp. 3–11.CrossRefGoogle Scholar
William, B., Bo, N. and Michael, C. M. (2006). Navigation Performance in High Earth Orbits Using Navigator GPS Receiver. 29th Annual AAS Guidance and Control Conference, Breckenridge, CO, United States, February 4–8.Google Scholar
Zou, X. J., Lian, B. W., Wu, P., Xu, H. W. and Zhang, L. (2017). Detect and Remove the Blocked Channel in the Vector Tracking Loop based on Carrier to Noise Density Ratio. Proc. ION GNSS, Institute of Navigation, Portland, Oregon, USA, September 25–29, pp. 3648–3660.CrossRefGoogle Scholar