Skip to main content
SpringerLink
Log in

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle’s experiences, application of PSOF algorithm in the INS/GPS integration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estimating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. A. Noureldin, T. B. Karamat, J. Georgy. Fundamentals of Inertial Navigation, Satellite-Based Positioning and their Integration, Berlin Heidelberg, Germany: Springer-Verlag, 2013. DOI: https://doi.org/10.1007/978-3-642-30466-8.

    Book  Google Scholar 

  2. R. Wang, Z. Xiong, J. Y. Liu, L. J. Shi. A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multistars observations. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 230, no. 4, pp. 698–712, 2016. DOI: https://doi.org/10.1177/0954410015596010.

    Article  Google Scholar 

  3. F. Xie, J. Y. Liu, R. B. Li, B. Jiang, L. Qiao. Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 229, no. 1, pp. 56–71, 2015. DOI: https://doi.org/10.1177/0954410014525359.

    Article  Google Scholar 

  4. H. Xie, K. H. Low, Z. He. Adaptive visual servoing of unmanned aerial vehicles in GPS-denied environments. IEEE/ASME Transactions on Mechatronics, vol. 22, no. 6, pp. 2554–2563, 2017. DOI: https://doi.org/10.1109/TMECH.2017.2755669.

    Article  Google Scholar 

  5. G. G. Hu, S. S. Gao, Y. M. Zhong, B. B. Gao, A. Subic. Matrix weighted multisensor data fusion for INS/GNSS/ CNS integration. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 230, no. 6, pp. 1011–1026, 2016. DOI: https://doi.org/10.1177/0954410015602723.

    Article  Google Scholar 

  6. C. R. Ashokkumar, G. W. P. York. Sensor fusions for constant thrust aircraft navigation in pitch plane. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 232, no. 2, pp. 388–398, 2018. DOI: https://doi.org/10.1177/0954410016683411.

    Article  Google Scholar 

  7. N. Gageik, M. Strohmeier, S. Montenegro. An autonomous UAV with an optical flow sensor for positioning and navigation. International Journal of Advanced Robotic Systems, vol. 10, no. 10, pp. 1–9, 2013. DOI: https://doi.org/10.5772/56813.

    Article  Google Scholar 

  8. Y. Kim, J. An, J. Lee. Robust navigational system for a transporter using GPS/INS fusion. IEEE Transactions on Industrial Electronics, vol. 65, no. 4, pp. 3346–3354, 2018. DOI: https://doi.org/10.1109/TIE.2017.2752137.

    Article  Google Scholar 

  9. D. Nada, M. Bousbia-Salah, M. Bettayeb. Multi-sensor data fusion for wheelchair position estimation with unscented Kalman filter. International Journal of Automation and Computing, vol. 15, no. 2, pp. 207–217, 2018. DOI: https://doi.org/10.1007/s11633-017-1065-z.

    Article  Google Scholar 

  10. A. M. Hasan, K. Samsudin, A. R. Ramli. Intelligently tuned wavelet parameters for GPS/INS error estimation. International Journal of Automation and Computing, vol. 8, no. 4, pp. 411–420, 2011. DOI: https://doi.org/10.1007/s11633-011-0598-9.

    Article  Google Scholar 

  11. J. A. Isaza, H. A. Botero, H. Alvarez. State estimation using nonuniform and delayed information: A review. International Journal of Automation and Computing, vol. 15, no. 2, pp. 125–141, 2018. DOI: https://doi.org/10.1007/s11633-017-1106-7.

    Article  Google Scholar 

  12. M. Enkhtur, S. Y. Cho, K. H. Kim. Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System. ETRI Journal, vol. 35, no. 5, pp. 943–946, 2013. DOI: https://doi.org/10.4218/etrij.13.0212.0540.

    Article  Google Scholar 

  13. D. Simon. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, New Jersey, USA: John Wiley & Sons, 2006.

    Book  Google Scholar 

  14. Y. L. Lin, W. D. Chang, J. G. Hsieh. A particle swarm optimization approach to nonlinear rational filter modeling. Expert Systems with Applications, vol. 34, no. 2, pp. 1194–1199, 2008. DOI: https://doi.org/10.1016/j.eswa.2006.12.004.

    Article  Google Scholar 

  15. M. A. Abido. Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, vol. 24, no. 7, pp. 563–571, 2002. DOI: https://doi.org/10.1016/S0142-0615(01)00067-9.

    Article  Google Scholar 

  16. T. H. Kim, C. S. Sin, S. Lee. Analysis of effect of spoofing signal in GPS receiver. In Proceedings of the 12th International Conference on Control, Automation and Systems, IEEE, JeJu Island, South Korea, pp. 2083–2087, 2012.

    Google Scholar 

  17. A. Jafarnia-Jahromi, S. Daneshmand, G. Lachapelle. Spoofing countermeasure for GNSS receivers–A review of current and future research trends. In Proceedings of the 4th International Colloquium on Scientific and Fundamental Aspects of the Galileo Programme, Prague, Czech, pp. 4–6, 2013.

    Google Scholar 

  18. A. Ranganathan, H. Ólafsdóttir, S. Capkun. SPREE: A spoofing resistant GPS receiver. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, ACM, New York City, USA, pp. 348–360, 2016. DOI: https://doi.org/10.1145/2973750.2973753.

    Google Scholar 

  19. J. Vasquez, B. Riggins. Detection of spoofing, jamming or failure of GPS. In Proceedings of the 49th Annual Meeting of The Institute of Navigation (1993), ION, Cambridge, USA, pp. 447–456, 1993.

    Google Scholar 

  20. A. R. Baziar, M. Moazedi, M. R. Mosavi. Analysis of single frequency GPS receiver under delay and combining spoofing algorithm. Wireless Personal Communications, vol. 83, no. 3, pp. 1955–1970, 2015. DOI: https://doi.org/10.1007/s11277-015-2497-9.

    Article  Google Scholar 

  21. J. Magiera, R. Katulski. Detection and mitigation of GPS spoofing based on antenna array. Journal of Applied Research and Technology, vol. 13, no. 1, pp. 45–57, 2015. DOI: https://doi.org/10.1016/S1665-6423(15)30004-3.

    Article  Google Scholar 

  22. S. Khanafseh, N. Roshan, S. Langel, F. C. Chan, M. Joerger, B. Pervan. GPS spoofing detection using RAIM with INS coupling. In Proceedings of the IEEE/ION Position, Location and Navigation Symposium, Monterey, USA, pp. 1232–1239, 2014. DOI: https://doi.org/10.1109/PLANS.2014.6851498.

    Google Scholar 

  23. R. R. Wilcox. Introduction to Robust Estimation and Hypothesis Testing, 3rd ed., USA: Academic Press, 2011.

    MATH  Google Scholar 

  24. S. Maskell, N. Gordon. A tutorial on particle filters for proceedings of online nonlinear/non-Gaussian Bayesian tracking. IEE Target Tracking: Algorithms and Applications, Enschede, Netherlands: IET, pp. 2–15, 2001. DOI: https://doi.org/10.1049/ic:20010246.

    Google Scholar 

  25. B. Ristic, S. Arulampalam, N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications, Boston, USA: Artech House, 2004.

    MATH  Google Scholar 

  26. J. C. Zhou, S. Knedlik, O. Loffeld. INS/GPS tightly-coupled integration using adaptive unscented particle filter. The Journal of Navigation, vol. 63, no. 3, pp. 491–511, 2010. DOI: https://doi.org/10.1017/S0373463310000068.

    Article  Google Scholar 

  27. R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micro Machine and Human Science, IEEE, Nagoya, Japan, pp. 39–43, 1995. DOI: https://doi.org/10.1109/MHS.1995.494215.

    Chapter  Google Scholar 

  28. I. C. Trelea. The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters, vol. 85, no. 6, pp. 317–325, 2003. DOI: https://doi.org/10.1016/S0020-0190(02)00447-7.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Dr. Saeed Nasrollahi from the Sharif University of Technology in Tehran for providing constructive feedback, careful reading and compassionate guidance to improve the article, Ehya Yavari from the Institute of Robotics and Computer (IRC) in Malek-Ashtar University of Technology in the field of LORAN-C national project, for his supporting behavior and Dr. Mahdi Majidi, the faculty member of University of Kashan because of his careful advices to improve this article.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Malek-Ashtar University of Technology, Tehran, 15875-1774, Iran

    Mohammad Majidi

  2. Faculty of Electrical Engineering, Malek-Ashtar University of Technology, Tehran, 15875-1774, Iran

    Alireza Erfanian

  3. Faculty of Electrical Engineering, K.N.Toosi University of Technology, Tehran, 16315-1355, Iran

    Hamid Khaloozadeh

Authors
  1. Mohammad Majidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Erfanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Khaloozadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Erfanian.

Additional information

Recommended by Associate Editor Min Cheol Lee

Mohammad Majidi received the B. Sc. degree in electrical engineering from Yazd University, Iran in 2010, the M. Sc. degree in electrical engineering from Malek-Ashtar University of Technology, Iran in 2012. He is a Ph. D. degree candidate in electrical engineering in Malek-Ashtar University of Technology, Iran since 2013. He works on navigation systems such as INS, GPS and Loran-C systems. His current research is about navigation system integration and optimization algorithms in position estimating process. He passed his research period in Polytechnic University of Catalonia, Spain. He has published about 20 national and international refereed journal and conference papers.

His research interests include robotics, navigation, INS/GPS integration, estimation theories, optimization methods and UAV positioning.

Alireza Erfanian received the B. Sc. degree in solid state physics from Mashhad University, Iran in 1991, the M. Sc. degree in solid state physics from Shahid Beheshti University, Iran in 1994, the Ph. D. degree in electrical engineering from K. N. Toosi University of Technology, Iran in 2009. He is a faculty member of Malek-Ashtar University of Technology, Iran. He is a professor in the field of micro and Nano electronics in Malek-Ashtar University of Technology, Iran. He is the head of Electronic and Electrical Department in Malek-Ashtar University of Technology, Iran. He has published about 100 refereed national and international journal and conference papers.

His research interests include navigation, system integration, estimation theories, microelectronic and Nano devices.

Hamid Khaloozadeh received the B. Sc. degree in control engineering from Sharif University of Technology, Iran in 1990, the M. Sc. degree in control engineering from K. N. Toosi University of Technology, Iran in 1994, the Ph. D. degree in control engineering from Tarbiat Modares University, Iran in 1998. He is a faculty member and professor at K. N. Toosi University of Technology, Iran. He has published more than 100 refereed journal and conference papers.

His research interests include robotics, system identification, optimal control, adaptive control, stochastic estimation, control and navigation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majidi, M., Erfanian, A. & Khaloozadeh, H. A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions. Int. J. Autom. Comput. 15, 747–760 (2018). https://doi.org/10.1007/s11633-018-1137-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-018-1137-8

Keywords

Search

Navigation