Advertisement

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

  • Mohammad Majidi
  • Alireza Erfanian
  • Hamid Khaloozadeh
Research Article

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.

Keywords

Inertial navigation system (INS)/global positioning system (GPS) integration unmanned aerial vehicles (UAVs) position estimation spoofing particle based filters 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

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.

References

  1. [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: 10.1007/978-3-642-30466-8.CrossRefGoogle Scholar
  2. [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: 10.1177/0954410015596010.CrossRefGoogle Scholar
  3. [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: 10.1177/0954410014525359.CrossRefGoogle Scholar
  4. [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: 10.1109/TMECH.2017.2755669.CrossRefGoogle Scholar
  5. [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 Mech-anical Engineers, Part G: Journal of Aerospace Engineer-ing, vol. 230, no. 6, pp. 1011–1026, 2016. DOI: 10.1177/ 0954410015602723.CrossRefGoogle Scholar
  6. [6]
    C. R. Ashokkumar, G. W. P. York. Sensor fusions for con-stant 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: 10.1177/0954410016683411.CrossRefGoogle Scholar
  7. [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: 10.5772/56813.CrossRefGoogle Scholar
  8. [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: 10.1109/TIE.2017.2752137.CrossRefGoogle Scholar
  9. [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: 10.1007/s11633-017-1065-z.CrossRefGoogle Scholar
  10. [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: 10.1007/s11633-011-0598-9.CrossRefGoogle Scholar
  11. [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: 10.1007/s11633-017-1106-7.CrossRefGoogle Scholar
  12. [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: 10.4218/etrij.13.0212.0540.CrossRefGoogle Scholar
  13. [13]
    D. Simon. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, New Jersey, USA: John Wiley & Sons, 2006.CrossRefGoogle Scholar
  14. [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: 10.1016/j.eswa.2006.12.004.CrossRefGoogle Scholar
  15. [15]
    M. A. Abido. Optimal power flow using particle swarm op-timization. International Journal of Electrical Power & Energy Systems, vol. 24, no. 7, pp. 563–571, 2002. DOI: 10.1016/S0142-0615(01)00067-9.CrossRefGoogle Scholar
  16. [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. [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. [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: 10.1145/2973750.2973753.Google Scholar
  19. [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. [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: 10.1007/s11277-015-2497-9.CrossRefGoogle Scholar
  21. [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: 10.1016/S1665-6423(15)30004-3.CrossRefGoogle Scholar
  22. [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: 10.1109/PLANS.2014.6851498.Google Scholar
  23. [23]
    R. R. Wilcox. Introduction to Robust Estimation and Hy-pothesis Testing, 3rd ed., USA: Academic Press, 2011.Google Scholar
  24. [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: 10.1049/ic:20010246.Google Scholar
  25. [25]
    B. Ristic, S. Arulampalam, N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications, Boston, USA: Artech House, 2004.zbMATHGoogle Scholar
  26. [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: 10.1017/S0373463310000068.CrossRefGoogle Scholar
  27. [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: 10.1109/MHS.1995.494215.CrossRefGoogle Scholar
  28. [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: 10.1016/S0020-0190(02)00447-7.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringMalek-Ashtar University of TechnologyTehranIran
  2. 2.Faculty of Electrical EngineeringMalek-Ashtar University of TechnologyTehranIran
  3. 3.Faculty of Electrical EngineeringK.N.Toosi University of TechnologyTehranIran

Personalised recommendations