Spoofing is a pernicious type of intentional interference where a GNSS receiver is fooled into tracking counterfeit signals. Starting from the fact that the spoofer’s signals share the same direction of arrival, a spoofing detection technique based on the Sum of Squares of the double difference carrier phase measurements was introduced in the past. However, that technique fails to work when the receiver tracks only a subset of fake signals. Thus, in this thesis we have presented four algorithm such as follow:
At first, we have presented a new AoA-based method to detect this situation, based on the Dispersion of the Double Differences (D3), which has shown to be effective in case of such mixed tracking. The algorithm works with every antenna distance, provided that the hypothesis of short baseline is satisfied; its hardware requirements are the same as for the SoS detector. Successful preliminary tests have been conducted to verify its performance.
At second, the work is planned to further evolve in several directions: i) a comparative evaluation of performance in terms of false alarm rate and correct detection rate at various C/N0 levels, also in case of non-equal C/N0 levels; ii) an investigation on possible optimization strategies for the decision threshold 𝜉𝜉2; iii) a more formal evaluation of the detection performance of the D3 algorithm in terms of probability of false alarm and correct detection; iv) the use of the D3 algorithm as a trigger for a robust direction finding algorithm, used to estimate the direction of the spoofing source with respect to the victim receiver. Furthermore, the possibility of using the second baseline for direction finding, i.e., for the estimation of the AOA of the spoofing source 𝛼𝛼𝑐𝑐𝑐𝑐𝑢𝑢, will be investigated for certain operative conditions.
At third, this thesis presented the theoretical derivation of missed detection and false alarm probabilities for a GNSS spoofing detection algorithm based on the AOA estimation suitable for dual-antennas GNSS systems. The algorithm, named D3, is based on the evaluation of regions of similarity for the DD of the carrier phase measurements: when the DDs of at least three signals are contained in the same region, then they are evaluated as counterfeit signals. The analytical derivation of the detection threshold for a target pairwise missed detection probability has been demonstrated, along with the performance obtained by the algorithm and the benefits of some proposed modifications. Finally, has been used to check the validity of the theoretical results. In a set of experimental tests, the D3 algorithm proved to be able to reach a reliable detection of spoofing attacks both in static and dynamic scenarios and at different C/N0 values, provided that the employed GNSS receivers produce reliable carrier phase measurements. In this thesis we have presented a new metric to improve the performance of the Dispersion of Double Difference algorithm to detect GNSS spoofing attacks in case of mixed tracking. The new metric is based on a linear regression of the fractional phase double differences. Although the required hardware components are the same as for SoS detector and standard D3 algorithms, the performance of this version of the D3, indicated and LR-D3, is better and independent
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of the C/N0 and the antenna distance. In addition, our algorithm eliminates the use of 2 baselines which is mandatory in the standard D3 method to reduce false alarms.
At fourth we propose a more robust approach to detect these spoofers using GMM.
Our method still leverages the concept of AOA and requires multiple antennas.
However, since the classification threshold is automatically learnt by GMMs, the algorithm can easily adapt to different antenna geometries and satellite conditions.
Our classification success rate is higher than 95% for both fake and authentic signal patterns. The thesis also has presented a low cost multidirectional GNSS signal generation method. This method disables most modern and commonly used GNSS spoofing detection techniques. Simulation data were generated for each antenna respectively and captured using a Septentrio receiver. The results of simulation and testing with the AoA estimation method based on the double difference of phase measurements show that the satellites have phase displacement in case of attack similar to real satellite. With this method, the simulator is able to overcome the most advanced and efficient method of spoofed signal detection currently available based on the estimation of AoA of the satellite signal.
The direction of the signal and the location of interference source will be estimated in the future works. By building two or more array antenna systems and placing them in two different locations, it will be possible to detect the direction of the signal and estimate the source of the interference signal.
127 PUBLICATIONS
1. V.H. Nguyen, G. Falco, M. Nicola, and E. Falletti,(2018) “A dual antenna GNSS spoofing detector based on the dispersion of double difference measurements”, in Proc. Int. 9th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC), Noordwijk, Netherlands, Dec. 2018, 5-7, DOI: 10.1109/NAVITEC.2018.8642705.
2. Nguyen Van Hien, Nguyen Dinh Thuan, Hoang Van Hiep, La The Vinh, (2020)
“A Gaussian Mixture Model Based GNSS Spoofing Detector using Double Difference of Carrier Phase”, pp. 042–047, Vol. 144 (2020), Journal of Science and Technology of Technical Universities, 2020.
3. Nguyễn Văn Hiên, Cao Văn Toàn, Nguyễn Đình Thuận, Hoàng Văn Hiệp,(2020)
"Phương pháp sinh dữ liệu mô phỏng GNSS đa hướng sử dụng công nghệ vô tuyến điều khiển bằng phần mềm". 178-185, số Đặc san Viện Điện tử, 9 - 2020, Tạp chí Nghiên cứu Khoa học Công nghệ quân sự.
4. N. Van Hien, G. Falco, E. Falletti, M. Nicola and T. V. La (2020), “A Linear Regression Model of the Phase Double Differences to Improve the D3 Spoofing Detection Algorithm,” 2020 European Navigation Conference (ENC), 2020, pp. 1- 14, doi: 10.23919/ENC48637.2020.9317320.
5. E. Falletti, G. Falco, V. H. Nguyen and M. Nicola (2021), “Performance Analysis of the Dispersion of Double Differences Algorithm to Detect Single-Source GNSS Spoofing,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no.
5, pp. 2674-2688, Oct. 2021, doi: 10.1109/TAES.2021.3061822.
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