Positioning Robustness against GPS Spoofing by Utilizing INS and Loran-C Systems

Authors

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

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

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

Abstract

Nowadays, reliability of systems in designing new navigation systems for Unmanned Aerial Vehicles (UAVs) has significant importance. One way to increase the reliability of systems is simultaneous use of multiple systems to reach a particular purpose. The GPS spoofing attack is one of the most important threats for UAV navigation systems in war conditions. In this article, by using INS and Loran-C systems, an integrated architecture is proposed for positioning robustness against GPS spoofing attack which benefits from particle based algorithms. The major concept of the proposed architecture consists of two basic parts; the detection part which is done by hypothesis test and the compensation part which is done by applying data fusion algorithms in the integrated INS/Loran-C systems. In this architecture in addition to the system reliability improvement in the event of a failure of one of the systems, the positioning accuracy in the presence of GPS spoofing attack is increased. So the UAVs can be used with more reliability in hazardous condition with high risk of GPS spoofers. The proposed method validation is proved by Root Mean Square Error (RMSE) metric. The simulation results validate the functionality of the proposed architecture for two GPS spoofing attack scenarios, and also in the case that the GPS and Loran-C are in outage mode, simultaneously.

Keywords


[1] A. Jovanovic, C. Botteron and P. A. Fariné, “Multi-test detection and protection algorithm against spoofing attacks on GNSS receivers,” Rec. - IEEE PLANS, Position Locat. Navig. Symp., pp. 1258–1271, 2014.
[2] B. Montgomery, Paul; Humphreys, Todd; Ledvina, “Receiver-autonomous spoofing detection: Experimental results of a multi-antenna receiver defense against a portable civil GPS spoofer,” Proc. 22nd Int. Tech. Meet. Satell. Div. Inst. Navig., pp. 124–130, 2009.
[3] A. Jafarnia-Jahromi, S. Daneshmand and G. Lachapelle, “Spoofing Countermeasures for GNSS Receivers – A Review of Current and Future Research,” 4th Intern Colloq. Sci. Fundam. Asp. Galileo Program., pp. 4–6, 2013.
[4] A. Jafarnia-Jahromi, A. Broumandan, J. Nielsen and G. Lachapelle, “GPS vulnerability to spoofing threats and a review of antispoofing techniques,” Int. J. Navig. Obs., vol. 2012.
[5] S. Khanafseh, N. Roshan, S. Langel, F. C. Chan, M. Joerger and B. Pervan, “GPS spoofing detection using RAIM with INS coupling,” Rec. - IEEE PLANS, Position Locat. Navig. Symp., pp. 1232–1239, 2014.
[6] L. Chang, K. Li and B. Hu, “Huber’s M-estimation-based process uncertainty robust filter for integrated INS/GPS,” IEEE Sens. J., vol. 15, no. 6, pp. 3367–3374, 2015.
[7] M. Malleswaran, V. Vaidehi and N. Sivasankari, “A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques,” Aerosp. Sci. Technol., vol. 32, no. 1, pp. 169–179, 2014.
[8] L. Zhao, H. Qiu and Y. Feng, “Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system,” J. Int. Meas. Confed., vol. 80, no. 80, pp. 138–147, 2016.
[9] U. S. C. Guard, “Loran-C user handbook,” Dep. Transp. COMDTINST M, vol. 16562, 1980.
[10] B. J. Jacoby, P. W. Schick, F. Richwalski and K. Zamzow, “Advantages of a Combined GPS / Loran-C Precision Timing Receiver.”
[11] J. J. Pisano, P. K. Enge and P. L. Levin, “Using GPS to calibrate Loran-C,” IEEE Trans. Aerosp. Electron. Syst., vol. 27, no. 4, pp. 696–708, 1991.
[12] G. Johnson et al., “Performance Trials of an Integrated Loran / GPS / IMU Navigation System , Part II,” 2005.
[13] B. R. S. Arulampalam, Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004.
[14] G. Battistelli, L. Chisci, C. Fantacci, A. Farina and A. Graziano, “Consensus-based multiple-model Bayesian filtering for distributed tracking,” IET Radar, Sonar Navig., vol. 9, no. 4, pp. 401–410, 2015.
[15] S. Y. Cho and W. S. Choi, “Robust Positioning Technique in Low-Cost DR/GPS for Land Navigation,” IEEE Trans. Instrum. Meas., vol. 55, no. 4, pp. 1132–1142, 2006.
[16] K. Li, J. Zhao, X. Wang and L. Wang, “Federated ultra-tightly coupled GPS/INS integrated navigation system based on vector tracking for severe jamming environment,” IET Radar, Sonar Navig., vol. 10, no. 6, pp. 1030–1037, 2016.
[17] M. Zhong, J. Guo and Q. Cao, “On Designing PMI Kalman Filter for INS / GPS Integrated Systems With Unknown Sensor Errors,” IEEE Sens. J., vol. 15, no. 1, pp. 535–544, 2015.
[18] A. Noureldin, T. B. Karamat, and J. Georgy, Fundamentals of Inertial Navigation, Satellite-Based Positioning and their Integration. 2013.
[19] بهروز صفری‌نژادیان، مجتبی اسد، «ارائه دو فیلتر کالمن مرتبه کسری جدید برای سیستم‌های مرتبه کسری خطی در حضور نویز اندازه‌گیری رنگی»، مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 2، تابستان 1396.
[20] M. Enkhtur, S. Y. Cho and K.-H. Kim, “Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System,” ETRI J., vol. 35, no. 5, pp. 943–946, 2013.
[21] رمضان هاونگی، «موقعیت‌یابی ربات بر اساس فیلتر ذره‌ای بهبودیافته با فیلتر کالمن گروهی هوشمند و گام MCMC»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 4، زمستان 1395.
[22] T.-H. Kim, C. S. Sin and S. Lee, “Analysis of effect of spoofing signal in GPS receiver,” in 12th International Conference on Control, Automation and Systems (ICCAS), pp. 2083–2087, 2012.
[23] A. Ranganathan, H. Ólafsdóttir and S. Capkun, “SPREE: Spoofing Resistant GPS Receiver”, Mobicom’16, 2016.
[24] A. R. Baziar, M. Moazedi and M. R. Mosavi, “Analysis of single frequency GPS receiver under delay and combining spoofing algorithm,” Wirel. Pers. Commun., vol. 83, no. 3, pp. 1955–1970, 2015.
[25] S. Maskell and N. Gordon, “A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking,” in Target tracking: algorithms and applications (Ref. No. 2001/174), IEE, pp. 1–2, 2001.
[26] B. Ristic, M. S. Arulampalam and N. Gordon, Beyond the Kalman Filter. Particle Filters for Tracking Applications. 2004.
[27] R. C. Eberhart, J. Kennedy and others, “A new optimizer using particle swarm theory,” in Proceedings of the sixth international symposium on micro machine and human science, vol. 1, pp. 39–43, 1995.
[28] I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Inf. Process. Lett., vol. 85, no. 6, pp. 317–325, 2003.
[29] Y.-L. Lin, W.-D. Chang and J.-G. Hsieh, “A particle swarm optimization approach to nonlinear rational filter modeling,” Expert Syst. Appl., vol. 34, no. 2, pp. 1194–1199, 2008.
[30] P. Williams and D. Last, “On Loran-C Time-Difference to Co-ordinate Converters,” in In Proceedings-International Loran Association (ILA)-32nd Annual Convention and Technical Symposium, 2003.
[31] R. R. Wilcox, Introduction to Robust Estimation and Hypothesis Testing. Academic Press, 2011.
[32] S. Y. Braasch, “Realtime Migitation of GPS SA Errors Using LORAN-C,” Wild Goose Assoc. Annu. Conv. Tech. Symp., pp. 55–62, 1992.