Luận án tiến sĩ nghiên cứu phương pháp phát hiện và xác định vị trí nguồn can nhiễu lên tín hiệu định vị sử dụng vệ tinh

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN VAN HIEN DETECT AND LOCALIZE INTERFERENCE SOURCES FOR GLOBAL NAVIGATION SATELLITE SYSTEMS Major: Computer Engineering Code No: 9480106 COMPUTER ENGINEERING DISSERTATION SUPERVISORS: Assoc Prof Lã Thế Vinh Prof Fabio Dovis Hanoi STATEMENT OF ORIGINALITY AND AUTHENTICITY I hereby declare that all the content and organization of the thesis is the product of my own research and does not compromise in any way the rights of third parties, and all citations are explicitly specified from credible sources I further confirm that all the data and results in the thesis are performed on actual devices completely true and have never been published by anyone else SUPERVISORS AUTHOR Assoc.Prof Lã Thế Vinh Nguyễn Văn Hiên Prof Fabio Dovis i ACKNOWLEDGEMENTS First of all, I would like to sincerely thanks my supervisor Assoc.Prof La The Vinh, for his guiding, supporting and motivating me throughout the whole my PhD student time I would also like to express my gratitude to the members of the Navigation, Signal Analysis and Simulation (NavSAS) and Navis Centre In many ways, they have contributed to all the research activities presented in the thesis In particular, I want to express my gratitude to Dr Gianluca Falco and Dr Nguyen Dinh Thuan, their endless support and their huge knowledge have greatly contributed to my work And I'd like to express my gratitude to Dr Emanuela Falletti, who offered scientific guidance and suggestions to help me develope and finish my research during my period at NavSAS Thanks to Prof Fabio Dovis, who gave me important ideas and guided me to my research especially during my period at Politecnico Di Torino I sincere thanks to VINIF With the great financial support of the VINIF, my research conditions have greatly improved, and I am fully committed to the works with all of my creative energy This work was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.TS.129 I'd also like to thank the members of the dissertation committee for their insightful suggestions, which have helped me develop and finish this dissertation Last but not least, I am grateful to my parents and my wife for their unconditional love, encouragement, support and motivation, as well as for inspiring me to overcome all challenges and difficulties in order to finish this thesis ii TABLE OF CONTENTS STATEMENT OF ORIGINALITY AND AUTHENTICITY i ACKNOWLEDGEMENTS ii TABLE OF CONTENTS .iii LIST OF ACRONYMS vi LIST OF TABLES .viii LIST OF FIGURES ix ABSTRACT xiv INTRODUCTION 16 1.1 Overview 16 1.2 Motivation .17 1.3 Problem statement 18 1.4 Contribution .19 1.5 Thesis outline 20 RELATED WORK .21 2.1 Civil GNSS vulnerabilities to intentional interference .21 2.2 Radio Frequency Interference 23 2.3 GNSS Interference detection techniques 25 2.4 Spoofing detection techniques 26 2.4.1 Classification of spoofing threat .26 2.4.2 Spoofing detection algorithms 27 2.5 Conclusions 32 INTERMEDIATED GNSS SPOOFING DETECTOR BASED ON ANGLE OF ARRIVAL .33 3.1 Fundamental background of GNSS and Spoofing 33 3.1.1 GNSS positioning theory 33 3.1.2 GPS signal 34 3.1.3 GNSS receiver architecture .35 3.1.4 GNSS spoofing .35 iii 3.2 Detection of a subset of counterfeit GNSS signals based on the Dispersion of the Double Differences (D3) 37 3.2.1 Differential Carrier-Phase Model and SoS Detector .38 3.2.2 Sum of Squares Detector Based on Double Differences 40 3.2.3 Some Limitations of the SoS Detector 42 3.2.4 Detection Of A Subset Of Counterfeit Signals Based On The Dispersion Of The Double Differences (D3) 44 3.2.5 Determination of the Decision Threshold .45 3.2.6 Cycle slip monitoring: the Doppler shift monitor 47 3.2.7 Reducing the probability of incorrect decision by time averaging 48 3.2.8 Experimental Results 49 3.3 Performance Analysis of the Dispersion of Double Differences Algorithm to Detect Single-Source GNSS Spoofing 54 3.3.1 Theoretical analysis of performance and decision threshold 54 3.3.2 Performance evaluation of robust D3 implementations 65 3.3.3 Considerations on practical performance 69 3.3.4 Performance assessment 70 3.4 A Linear Regression Model of the Phase Double Differences to Improve the D3 Spoofing Detection Algorithm 78 3.4.1 Limitations of D3 algorithm 78 3.4.2 The piecewise linear model .80 3.4.3 The proposed LR-D3 detector 83 3.4.4 Performance assessment with in-lab GNSS signals 87 3.5 Conclusions 92 SOPHISTICATED GNSS SPOOFING DETECTOR BASED ON ANGLE OF ARRIVAL .94 4.1 Gaussian Mixture Models and Expectation-Maximization for GMM (source [76]) .94 4.1.1 Gaussian distribution .94 4.1.2 GMM Distribution 95 4.1.3 Maximum likelihood for the Gaussian 100 4.1.4 The expectation maximization algorithm for GMM 101 iv 4.2 A Gaussian Mixture Model Based GNSS Spoofing Detector using Double Difference of Carrier Phase in simple spoofing scenario .107 4.3 A novel approach to classify authentic and fake GNSS signals in sophisticated spoofing scenario using Gaussian Mixture Models 109 4.3.1 Grouping of Double Carrier Phase Difference 109 4.4 Multi-Directional GNSS Simulation Data Generation Method Use of Software Defined Radio Technology .114 4.4.1 Multidirectional GNSS signal simulation .114 4.4.2 Signal and system model .114 4.5 Experimental result 116 4.5.1 Multidirectional GNSS signals simulation 116 4.5.2 Sophisticated GNSS spoofing detector 118 4.6 Conclusions 123 CONCLUSIONS AND FUTURE WORKS 124 PUBLICATIONS 126 REFERENCES 127 v LIST OF ACRONYMS Acronym Meaning ADC Analog to Digital Converters AGC Automatic Gain Control AoA Angle of Arrival C/A Coarse/Acquisition C/N0 Carrier-to-Noise density CDMA Code Division Multiple Access D3 Dispersion of the Double Differences DVBT Digital Video Broadcasting – Terrestrial FDMA Frequency Division Multiple Access FNR False Negative Rate FPR False Positive Rate GLRT General Likelihood Ratio Test GMM Gaussian Mixture Model GNSS Global Navigation Satellite Systems GoF Goodness of Fit GPS Global Positioning System vi GSM Global System for Mobile Communications IMU Inertial Measurement Units OEM Original Equipment Manufacturer PVT Position, Velocity and Time RFI Radio Frequency Interference RX Receiver SDR Software-Defined Radio SIS Signal in Space SoS Sum of Squares TNR True Negative Rate TOA Time of Arrival TPR True Positive Rate TX Transmitter UTMS Universal Mobile Telecommunications System VHF Very High Frequency VSD Vestigial Signal Defense vii LIST OF TABLES Table 2.1 Techniques of GNSS spoofing detector based on signal features 29 Table 3.1 Percentage of correct decisions for SoS and D 3, in the three scenarios under test 52 Table 3.2 Statistical performance of the D3 algorithm with two baselines .67 Table 3.3 Static tests: estimation of the probability of missed detection on the counterfeit signals (%) the ‘overall’ case is the probability of missed detection of three counterfeit signals 71 Table 3.4 Static tests: Estimation of the probability of false alarms on the authentic signals (%) 72 Table 3.5 Dynamic tests: aircraft trajectories description 73 Table 3.6 Dynamic test TRJ1: Estimation of the probability of missed detection on the counterfeit signals (%) The ‘overall’ case is the probability of missed detection of three counterfeit signals .75 Table 3.7 Dynamic test TRJ1: Estimation of the probability of false alarm on the authentic signals (%) .75 Table 3.8 Dynamic test TRJ2: Estimation of the probability of missed detection on the counterfeit signals (%) .76 Table 3.9 Dynamic test TRJ2: Estimation of the probability of false alarm on the authentic signals (%) .76 Table 3.10 Static test with Real Measurements: Detection Results for Test #1 .77 Table 3.11 Dynamic tests with Real Measurements: Tests trajectories description 77 Table 3.12 Dynamic tests with Real Measurements: Detection Results for Test #4 78 Table 3.13 Comparison of detection performance for hours of signal simulation: LR-D3 and standard D3 algorithms 88 Table 3.14 Detection performance as a function of C/N0 .91 Table 4.1 The result of cross validation testing 119 Table 4.2 The result of Fractional DDs in case of Intermediate spoofing attack, where the DDs of authentic satellites cross the ones related to the spoofed satellites 121 viii Table 4.3 Normalized confusion matrix of Fractional DDs in case of Intermediate spoofing attack 123 LIST OF FIGURES Figure 1.1 Applications of GNSS (source: [64]) 16 Figure 2.1 The enviroment for transmitting signals from satellites to receivers (source: [65]) 21 Figure 2.2 The low SIS signal power of GNSS (source: [75]) .22 Figure 2.3 GNSS frequency bands (source:[69]) 22 Figure 2.4 Radio frequency interference 23 Figure 2.5 Intermediated Spoofing Scenario 24 Figure 2.6 Cheap jammers are widely sold online (source: [96]) 24 Figure 2.7 Techniques for Detecting GNSS Interference 25 Figure 2.8 Three continuum of spoofing threat: simplistic, intermediate, and sophisticated attacks (source:[19]) 26 Figure 2.9 A summary of the various spoofing detection methods available in the literature (source: [17]) 28 Figure 2.10 Angle of arrival of GNSS satellite 30 Figure 2.11 Angle of arrival defense Spoofing 31 Figure 3.1 Spherical positioning system of GNSS 33 Figure 3.2 A fundamental GNSS receiver architecture (source: [72]) 35 Figure 3.3 Principles of GPS simulator 36 Figure 3.4 Blocks scheme of GPS simulator 37 Figure 3.7 Reference geometry for the dual-antenna system 40 Figure 3.8 Fractional DDs and SoS detector results under simulated spoofing attack (H0) 41 Figure 3.9 Fractional DDs and SoS detector results in normal conditions (H1) .42 Figure 3.10 Fractional DD measurements and SoS detection metric in mixed tracking conditions under spoofing attack Only three signals out of nine are counterfeit The reference signal is authentic 43 ix From Figure 28 (b), it is possible to see that the spoofer is located on a mezzanine at ISMB premises and comprises of a hardware simulator, a PC laptop running the SW part of the GNSS simulator and a choke ring passive Novatel antenna transmitting the amplified GNSS-like signals In Figure 28 (a) and (c), we can see the spoofing signal is received by a set of three antennas (forming two baselines) that are connected to two multi-constellation dual-antenna Septentrio receivers It is important to stress that only one baseline would be necessary to detect the spoofing attack (2) Cross validation testing In this work we use the cross validation to divide the data into 10 folds We use folds to training folds as a testing data With the data shown in Figure 72, when we use the GMM model to detect fake signals We obtained the results as shown in Table 16 and Table 17 From Table 16 we see the results to identify the spoofing signal with high performance without depending on the C/N0 value as algorithm D3 Figure 86 shows the case of a DD of real satellite cross DD of fake satellites With D3 algorithm in the time period of 148s-152s, the system gets false alarm the real satellite PRN25 into a fake satellite as shown in Figure 86 Using D3 spoofing detector reaches only 98.02% efficiency and this algorithm depends on C/N0 value Figure 4.86 Fractional DDs in case of Intermediate spoofing attack, where the DDs of authentic satellites (PRN 25) cross the ones related to the spoofed satellites 123 Figure 4.87 False alarm in the D3 detector: a fractional DD from a genuine satellite crosses the DDs of the spoofed satellites With the data in Figure 86, when we use GMM we get a much better result than D3 (Figure 87) without dependent the C/N0 value as shown in Figure 86 And in the False Alarm Rate and Miss Detection Rate are approximately 2% as shown in Error: Reference source not found Table 4.17 The result of Fractional DDs in case of Intermediate spoofing attack, where the DDs of authentic satellites cross the ones related to the spoofed satellites C/N0 = 39 Fold number Number of training data points Number of testing data points Number of correctlyclassified points Accuracy 1550 174 172 98.85% 10 Total 1550 1550 1550 1550 1550 1550 1550 1550 1550 174 174 174 174 174 174 174 174 174 172 171 171 172 171 172 172 172 172 98.85% 98.27% 98.27% 98.85% 98.27% 98.85% 98.85% 98.85% 98.85% 98.62% (σ2 =0.088) 124 C/N0 = 42 Fold number 10 Number of training data points 1550 1550 1550 1550 1550 1550 1550 1550 1550 1550 Number of testing data points 174 174 174 174 174 174 174 174 174 174 Total Number of correctlyclassified points 173 172 172 171 173 173 172 171 171 172 Accuracy 99.43% 98.85% 98.85% 98.27% 99.43% 99.43% 98.85% 98.27% 98.27% 98.85% 98.85% (σ2 =0.088) C/N0 = 45 Fold number 10 Number of training data points 1550 1550 1550 1550 1550 1550 1550 1550 1550 1550 Number of testing data points 174 174 174 174 174 174 174 174 174 174 Total 125 Number of correctlyclassified points 172 171 172 172 171 173 171 172 171 172 Accuracy 98.85% 98.27% 98.85% 98.85% 98.27% 99.43% 98.27% 98.85% 98.27% 98.85% 98.68% (σ2=0.088) Table 4.18 Normalized confusion matrix of Fractional DDs in case of Intermediate spoofing attack Predicted as spoofed signal Predicted as authentic signal Actual: spoofed signal TPR=98.2% FNR=1.8% Actual: authentic signal FPR= 1.59% TNR=99=98.41% 4.6 Conclusions A more robust strategy to detecting these spoofers using GMM was proposed in this chapter GMM was proposed as a more reliable method of detecting these GNSS spoofing signal The AOA principle is still used in our method, and the data of two antennas GMM can easily adjust to changing antenna geometries and satellite conditions since they learn the classification threshold automatically Our classification success rate is better than 95% for both spoofed and authentic signals The thesis also includes a low-cost multidirectional GNSS signal generation method This technique disables the most recent and frequently used GNSS spoofing detection methods A Septentrio receiver was used to capture simulation data for each antenna The primary research findings from Chapter have been published in articles and of the publications: 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 Nguyễn Văn Hiên, Cao Văn Tồn, Ngũn Đình Thuận, Hoàng Văn Hiệp, (2020) "Phương pháp sinh liệu mô GNSS đa hướng sử dụng công nghệ vô tuyến điều khiển phần mềm" 178-185, số Đặc san Viện Điện tử, - 2020, Tạp chí Nghiên cứu Khoa học Công nghệ quân 126 CONCLUSIONS AND FUTURE WORKS 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 (D 3), 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/N levels; ii) an investigation on possible optimization strategies for the decision threshold ξ 2❑; iii) a more formal evaluation of the detection performance of the D 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 α cnt , 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 D 3, 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 D algorithm proved to be able to reach a reliable detection of spoofing attacks both in static and dynamic scenarios and at different C/N 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 127 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 LRD3, is better and independent of the C/N and the antenna distance In addition, our algorithm eliminates the use of baselines which is mandatory in the standard D 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 128 PUBLICATIONS 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 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 Nguyễn Văn Hiên, Cao Văn Tồn, Ngũn Đình Thuận, Hồng Văn Hiệp, (2020) "Phương pháp sinh liệu mô GNSS đa hướng sử dụng công nghệ vô tuyến điều khiển phần mềm" 178-185, số Đặc san Viện Điện tử, - 2020, Tạp chí Nghiên cứu Khoa học Công nghệ quân Van Hien Nguyen, Gianluca Falco, Emanuela Falletti, Mario Nicola, The Vinh La, (2021) “A Linear Regression Model of the Phase Double Differences to Improve the D3 Spoofing Detection Algorithm” European Navigation Conference 2020, 23-24 November 2020, Dresden, Germany Date Added to IEEE Xplore: 18 January 2021 E Falletti, G Falco, Van Hien Nguyen, M Nicola (2021), “Performance Analysis of the Dispersion of Double Differences Algorithm to Detect GNSS Spoofing” IEEE Transactions on Aerospace and Electronic Systems Early Access Print ISSN: 0018-9251 Online ISSN: 1557-9603 DOI: 10.1109/TAES.2021.3061822 129 REFERENCES [1] F Dovis, “GNSS Interference Threats and Countermeasures” Norwood, MA, USA: Artech House, 2015 [2] E Falletti, D 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