Anti-spoofing by smart acquisition in cold-start with multiple hypothesis using wavelet transform in a software GPS receiver

Document Type : Research Paper

Authors

1 Department of Engineering Sciences, Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran

2 Department of Electrical Engineering, Iran University of Science and Technology,

Abstract

The spoofing subject is becoming ever increasingly more severe. In addition to the flow of technology, the availability of software-defined radio platforms has increased. Usually, detecting the spoofing is performed by introducing the features that are difficult for the deceiver to counterfeit. Spoofing and countering can be performed in different parts of a GPS receiver. In recent years, less attention has been paid to defense at cold-start. This research presents that the spoofing attack can be diminished during the initial start-up process with a very short effective time. This low-cost method introduces a new decision rule based on a multiple statistical hypothesis test to identify fake peaks in correlation output of acquisition and extract the authentic peaks utilizing the wavelet transform or peak removal process. The main distinction of this method with previous works is investigating different amplitude ratios of spoofing signal to authentic. Simulation results on 10 data sets show that the probability of correct detection and mitigation is more than 90%.

Keywords

[1] A.R. Baziar, M. Moazedi, and M.R. Mosavi, Analysis of single frequency GPS receiver under delay and combining spoofing algorithm, Wireless Person. Commun. 83 (2015), 1955–1970.
[2] D. Borio, L. Camoriano, and L.L. Presti, Impact of GPS acquisition strategy on decision probabilities, IEEE Trans. Aerospace Electronic Syst. 44 (2008), no. 3, 996–1011.
[3] K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach, Springer Science and Business Media, 2007.
[4] A. Broumandan, A. Jafarnia-Jahromi, S. Daneshmand, and G. Lachapelle, Overview of spatial processing approaches for GNSS structural interference detection and mitigation, Proc. IEEE 104 (2016), no. 6, 1246–1257.
[5] F. Dovis, GNSS Interference Threats and Countermeasures, Artech House, 2015.
[6] E. Garbin Manfredini and F. Dovis, On the use of a feedback tracking architecture for satellite navigation spoofing detection, Sensors 16 (2016), no. 12, 2051.
[7] Y. Hu, S. Bian, K. Cao, and B. Ji, GNSS spoofing detection based on new signal quality assessment model, GPS Solutions 22 (2018), 1–13.
[8] A. Jafarnia Jahromi, GNSS signal authenticity verification in the presence of structural interference, PhD diss., University of Calgary, 2013.
[9] E.D. Kaplan and C.J. Hegarty, Understanding GPS: Principles and Applications, Norwood, MA: Artech House, 2017.
[10] F. Lazaro, R. Raulefs, H. Bartz, and T. Jerkovits, VDES R-Mode: Vulnerability analysis and mitigation concepts, Int. J. Satellite Commun. Network. 41 (2023), no. 2, 178–194.
[11] M. Li, Y. Yuan, N. Wang, Z. Li, Y. Li, and X. Huo. Estimation and analysis of Galileo differential code biases, J. Geodesy 91 (2017), 279–293.
[12] C. Liang, M. Miao, J. Ma, H. Yan, Q. Zhang, and X. Li, Detection of global positioning system spoofing attack on unmanned aerial vehicle system, Concurren. Comput.: Practice Experience 34 (2022), no. 7, e5925.
[13] A. Polydoros, On the synchronization aspects of direct-sequence spread spectrum systems, Ph.D. dissertation, Department of Electrical Engineering, University of Southern California, 1982.
[14] S. Semanjski, I. Semanjski, W.D. Wilde, and A. Muls, Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data—Part I, Sensors 20 (2020), no. 4, 1171.
[15] E. Schmidt, Z. Ruble, D. Akopian, and D.J. Pack, Software-defined radio GNSS instrumentation for spoofing mitigation: A review and a case study, IEEE Trans. Instrument. Measurement 68 (2018), no. 8, 2768–2784.
[16] E. Schmidt, Z.A. Ruble, D. Akopian, and D.J. Pack, A reduced complexity cross-correlation interference mitigation technique on a real-time software-defined radio GPS L1 receiver, IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE, 2018, pp. 931–939.
[17] E. Shafiee, M.R. Mosavi, and M. Moazedi, Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers, J. Navigation 71 (2018), no. 1, 169-188.
[18] M. Sharie, M.R. Mosavi, and N. Rahemi, Acquisition of weak GPS signals using wavelet-based de-noising methods, Survey Rev. 52 (2020), no. 375497–375513.
[19] M. Sharie, M.R. Mosavi, and N. Rahemi, Determination of an appropriate mother wavelet for de-noising of weak GPS correlation signals based on similarity measurements, Engin. Sci. Technol.: Int. J. 23 (2020), no. 2 281–288.
[20] Z. Wu, Y. Zhang, and R. Liu, BD-II NMA and SSI: An scheme of anti-spoofing and open BeiDou II D2 navigation message authentication, IEEE Access 8 (2020), 23759–23775.
[21] D. Yuan, H. Li, F. Wang, and M. Lu, A GNSS acquisition method with the capability of spoofing detection and mitigation Chin. J. Electron. 27 (2018), no. 1, 213–222.
[22] L.I. Yang-zhi, L. Guangxia, and C. Jian, The research and modification of the cascaded FFT acquisition algorithm, Signal Process. 27 (2011), no. 5, 721-726.
Volume 16, Issue 1
January 2025
Pages 147-161
  • Receive Date: 17 December 2023
  • Accept Date: 01 February 2024