Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T07:19:05.589Z Has data issue: false hasContentIssue false
  • English
  • Français

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

Published online by Cambridge University Press:  17 March 2020

M. A. Farhad ,
M. R. Mosavi Open the ORCID record for M. R. Mosavi [Opens in a new window] ,
A. A. Abedi  and
K. Mohammadi
M. A. Farhad
Affiliation:
(School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran)
M. R. Mosavi*
Affiliation:
(School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran)
A. A. Abedi
Affiliation:
(School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran)
K. Mohammadi
Affiliation:
(School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking.

Keywords

FuzzyGNSSWeak SignalKalman Filter
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 991 - 1013
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

Brewer, J. and Raquet, J. (2016). Differential vector phase locked loop. IEEE Transactions on Aerospace and Electronic Systems, 52(3), 1046–1055.CrossRefGoogle Scholar
Chen, X., Wang, X. and Xu, Y. (2014). Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter. Journal of Sensors, 14(12), 23630–23649.CrossRefGoogle ScholarPubMed
Groves, P. D. and Mather, C. J. (2010). Receiver interface requirements for deep INS/GNSS integration and vector tracking. Journal of Navigation, 63(03), 471–489.CrossRefGoogle Scholar
Hu, S., Xu, S., Wang, D. and Zhang, A. (2015). Optimization algorithm for Kalman filter exploiting the numerical characteristics of SINS/GPS integrated navigation systems. Journal of Sensors, 15(11), 28402–28420.CrossRefGoogle ScholarPubMed
Jiang, R., Wang, K. and Wang, J. (2017). Performance analysis and design of the optimal frequency-assisted phase tracking loop. GPS Solutions, 21(2), 759–768. http://dx.doi.org/10.1007/s10291-016-0565-6.CrossRefGoogle Scholar
Jwo, D. J. and Wang, S. H. (2007). Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sensors, 7(3), 778–789.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 Modeling, 39(19), 5949–5968.CrossRefGoogle Scholar
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), 661–672.CrossRefGoogle Scholar
Li, K., Zhao, J., Wang, X. and Wang, L. (2016). Federated ultra-tightly coupled GPS/INS integrated navigation system based on vector tracking for severe jamming environment. Journal of IET Radar, Sonar & Navigation, 10(6), 1030–1037.CrossRefGoogle Scholar
Lim, D. W., Kang, H. W., Cho, S. L., Lee, S. J. and Heo, M. B. (2013). Performance Evaluation of a GPS Receiver with VDFLL in Harsh Environments. International Global Navigation Satellite Systems Society IGNSS Symposium, Gold Coast, Queensland, Australia, July 16–18, 1–12.Google Scholar
Liu, G., Zhang, R., Guo, M. and Cui, X. (2014). Accuracy Comparison of GNSS Vector and Scalar Tracking Loop. Guidance, Navigation and Control Conference (CGNCC), Yantai, China, August 8–10, 1346–1351.CrossRefGoogle Scholar
Liu, Q., Huang, Z., Kou, Y. and Wang, J. (2018). A low-ambiguity signal waveform for pseudolite positioning systems based on chirp. Journal of Sensors, 18(5), 1326–1344. http://dx.doi.org/10.3390/s18051326.CrossRefGoogle ScholarPubMed
Liu, Q., Kou, Y., Huang, Z., Wang, J. and Yao, Y. (2019a). Mean acquisition time analysis for GNSS parallel and hybrid search strategies. GPS Solutions, 23(4), 94–106. https://doi.org/10.1007/s10291-019-0883-6.CrossRefGoogle Scholar
Liu, Q., Huang, Z. and Wang, J. (2019b). Indoor non-line-of-sight and multipath detection using deep learning approach. GPS Solutions, 23(3), 75–88. https://doi.org/10.1007/s10291-019-0869-4.CrossRefGoogle Scholar
Luo, Y., Wu, W., Babu, R., Tang, K. and Luo, B. (2012). A simplified baseband prefilter model with adaptive Kalman filter for ultra-tight COMPASS/INS integration. Journal of Sensors, 12(7), 9666–9686.CrossRefGoogle ScholarPubMed
Mao, W. L. (2007). Applications of new fuzzy inference-based tracking loops for kinematic GPS receiver. Journal of Circuits, Systems & Signal Processing, 26(1), 91–113.CrossRefGoogle Scholar
Mohamed, A. H. and Schwarz, K. P. (1999). Adaptive Kalman filtering for INS/GPS. Geodesy, 73(4), 193–203.CrossRefGoogle Scholar
Mosavi, M. R., Moazedi, M., Rezaei, M. J. and Tabatabaei, A. (2015). Interference Mitigation in GPS Receivers. Tehran, Iran: Iran University of Science and Technology.Google Scholar
Nourmohammadi, H. and Keighobadi, J. (2018). Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system. Journal of Mechanical Systems and Signal Processing, 99(1), 434–449.CrossRefGoogle Scholar
Parkinson, B., Spilker, J., Axelrad, P. and Enge, P. (1996). Global Positioning System: Theory and Applications. Washington, DC: American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
Petovello, C., Petovello, M. G. and Lachapelle, G. (2011). Choosing the coherent integration time for Kalman filter-based carrier-phase tracking of GNSS signals. Journal of GPS Solutions, 15(4), 345–356.Google Scholar
Psiaki, M. L. and Jung, H. (2002). Extended Kalman Filter Methods for Tracking Weak GPS Signals. Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation, Oregon Convention Center, Portland, OR, September 24–27, 2539–2554.Google Scholar
Scherer, R. (2012). Multiple Fuzzy Classification Systems. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Soloview, A., Grass, F. V. and Gunawardena, S. (2009). Decoding navigation data messages from weak GPS signals. IEEE Transactions on Aerospace and Electronic Systems, 45(2), 660–667.CrossRefGoogle Scholar
Song, Y. and Lian, B. (2016). Combined BDS and GPS Adaptive Vector Tracking Loop in Challenge Environment. The 7th China Satellite Navigation Conference, Changsha, China, May 18–20, 557–570.CrossRefGoogle Scholar
Spilker, J. J. (1996). Fundamentals of signal tracking theory. In Parkinson, B. W. (ed.), Global Positioning System: Theory and Applications, Volume 1, volume 163 of Progress in Astronautics and Aeronautics, chapter 4. Washington, DC: American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
Tabatabaei, A., Mosavi, M. R., Khavari, A. and Shahhoseini, H. S. (2016). Reliable urban canyon navigation solution in GPS and GLONASS integrated receiver using improved fuzzy weighted least-square method. Journal of Wireless Personal Communications, 94(4), 3181–3196.CrossRefGoogle Scholar
Tabatabaei, A., Mosavi, M. R., Shahhoseini, H. S. and Borre, K. (2017). Vectorized and federated software receivers combining GLONASS and GPS. Journal of GPS Solutions, 21(3), 1331–1339.CrossRefGoogle Scholar
Wang, X., Ji, X., Feng, S. and Calmettes, V. (2015). A high-sensitivity GPS receiver carrier-tracking loop design for high-dynamic applications. Journal of GPS Solutions, 19(2), 225–236.CrossRefGoogle Scholar
Won, J. H., Dötterböck, D. and Eissfeller, B. (2010). Performance comparison of different forms of Kalman filter approaches for a vector-based GNSS signal tracking loop. Journal of the Institute of Navigation, 57(3), 185–190.CrossRefGoogle Scholar
Xu, B. and Hsu, L. T. (2019). Open-source MATLAB code for GPS vector tracking on a software defined receiver. Journal of GPS Solutions, 23(2), 23–46.CrossRefGoogle Scholar
Zhao, S. and Akos, D. M. (2011). An Open Source GPS/GNSS Vector Tracking Loop-Implementation, Filter Tuning and Results. Proceedings of the 2011 International Technical Meeting, Institute of Navigation, San Diego, CA, January 24–26, 1293–1305.Google Scholar
Zhao, S. and Lu, M. (2012). GNSS vector lock loop based on adaptive Kalman filter. Journal of Harbin Institute of Technology, 44(7), 141–143.Google Scholar