Detect and localize interference sources for global navigation satellite systems

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 La The Vinh Assoc Prof Fabio Dovis Hanoi -2022 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 Hanoi, August 2022 SUPERVISORS AUTHOR Assoc.Prof Lã Thế Vinh Nguyễn Văn Hiên Prof Fabio Dovis i ACKNOWLEDGEMENTS First of all, I would like to thanks my supervisor Assoc.Prof La The Vinh sincerely, 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 Mainly, I want to express my gratitude to Dr Gianluca Falco and Dr Nguyen Dinh Thuan, their endless support and huge knowledge have greatly contributed to my work And I would 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 Assoc.Prof Fabio Dovis, who gave me important ideas and guided me to my research especially during my period at Politecnico Di Torino I sincerely 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 would 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 Scope of Research 19 1.5 Contribution 19 1.6 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 [67]) 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 (source [67]) 101 iv 4.2 A Gaussian Mixture Model Based GNSS Spoofing Detector using Double Difference of Carrier Phase in simple spoofing scenario 108 4.3 A novel approach to classify authentic and fake GNSS signals in sophisticated spoofing scenario using Gaussian Mixture Model 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 115 4.4.1 Multidirectional GNSS signal simulation 115 4.4.2 Signal and system model 116 4.5 Experimental result 117 4.5.1 Multidirectional GNSS signals simulation 117 4.5.2 Sophisticated GNSS spoofing detector 120 4.6 Conclusions 123 CONCLUSIONS AND FUTURE WORKS 125 PUBLICATIONS 127 REFERENCES 128 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 GSM Global System for Mobile Communications vi 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 D3, 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 120 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 122 Table 4.3 Normalized confusion matrix of Fractional DDs in case of Intermediate spoofing attack 123 viii LIST OF FIGURES Figure 1.1 Applications of GNSS (source:[12]) 16 Figure 2.1 The enviroment for transmitting signals from satellites to receivers (source: [33]) 21 Figure 2.2 The low SIS signal power of GNSS (source: [35]) 22 Figure 2.3 GNSS frequency bands (source: [36]) 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: [38]) 24 Figure 2.7 Techniques for Detecting GNSS Interference 25 Figure 2.8 Three continuum of spoofing threat: simplistic, intermediate, and sophisticated attacks (source: [27]) 26 Figure 2.9 A summary of the various spoofing detection methods available in the literature (source: [13]) 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: [46]) 35 Figure 3.3 Principles of GPS simulator 36 Figure 3.4 Blocks scheme of GPS simulator 37 Figure 3.5 Block diagram of SoS Detector 38 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 Figure 3.11 Example of cycle slips effect on the SoS metric in the presence of single source The detector is not able to reveal a spoofing attack when cycle slips occur 43 Figure 3.12 Zero baseline fractional DD measurements for various values of input C/N0 ratio In this setup the ratio was equal for all the simulated signals 46 ix signal with high performance and high accuracy with 99.99% rate without depending on the C/N0 value as algorithm D3 Figure 4.24 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 4.24 Using D3 spoofing detector reaches only 98.02% efficiency and this algorithm depends on C/N0 value Figure 4.24 Fractional DDs in case of Intermediate spoofing attack, where the DDs of authentic satellites (PRN 25) cross the ones related to the spoofed satellites Figure 4.25 False alarm in the D3 detector: a fractional DD from a genuine satellite crosses the DDs of the spoofed satellites 121 With the data in Figure 4.24, when we use GMM we get a much better result than D3 (Figure 4.25) without dependent the C/N0 value as shown in Figure 4.24 And in the False Alarm Rate and Miss Detection Rate are approximately 2% as shown in Table 4.3 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 C/N0 = 39 Fold number 10 Total 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 Number of correctlyclassified points 172 172 171 171 172 171 172 172 172 172 Accuracy 98.85% 98.85% 98.27% 98.27% 98.85% 98.27% 98.85% 98.85% 98.85% 98.85% 98.62% (σ2 =0.088) C/N0 = 42 Fold number 10 Total Number of training data points 1550 1550 1550 1550 1550 1550 1550 1550 1550 1550 Number of testing data points 174 174 174 174 174 174 174 174 174 174 122 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 Number of correctlyclassified points 172 171 172 172 171 173 171 172 171 172 Total 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 4.3 Normalized confusion matrix of Fractional DDs in case of Intermediate spoofing attack Actual: spoofed signal Actual: authentic signal Predicted as spoofed signal TPR=98.2% FPR= 1.59% Predicted as authentic signal FNR=1.8% 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 123 Difference of Carrier Phase”, pp 042–047, Vol 144 (2020), Journal of Science and Technology of Technical Universities, 2020 Nguyễn Văn Hiên, Cao Văn Toàn, Nguyễn 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 124 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 (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 𝜉𝜉 ; 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 𝛼𝛼𝑐𝑐𝑐𝑐𝑐𝑐 , 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 125 of the C/N0 and the antenna distance In addition, our algorithm eliminates the use of 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 126 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 Toàn, Nguyễn 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 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 114, doi: 10.23919/ENC48637.2020.9317320 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 127 REFERENCES [1] F Dovis, “GNSS Interference Threats and Countermeasures” Norwood, MA, USA: Artech House, 2015 [2] E Falletti, D Margaria, G Marucco, B Motella, M Nicola, M Pini, “Synchronization of critical infrastructures dependent upon GNSS: current vulnerabilities and protection provided by new signals”, manuscript under review to the IEEE Systems Journal, submitted on Feb 19, 2018 [3] Wang, Yue, et al "Design and implementation of programmable multi-mode GNSS signal simulator." 2010 IEEE 12th International Conference on Communication Technology IEEE, 2010 [4] C Tanil, P Martinez Jimenez, M Raveloharison, B Kujur, S Khanafseh, and B Pervan, “Experimental Validation of INS Monitor against GNSS Spoofing,” in ION GNSS+ 2018, 2018 [5] Liu, Yang, et al "Impact assessment of GNSS spoofing attacks on ins/GNSS integrated navigation system." Sensors 18.5 (2018): 1433 [6] Wang, Fei, Hong Li, and Mingquan Lu "GNSS spoofing countermeasure with a single rotating antenna." IEEE Access (2017): 8039-8047 [7] Huang, Jie, et al "GNSS spoofing detection: Theoretical analysis and performance of the Ratio Test metric in open sky." Ict Express 2.1 (2016): 3740 [8] P Y Montgomery, T E Humphreys, and B M Ledvina, “Receiverautonomous spoofing detection: Experimental results of a multi-antenna receiver defense against a portable civil GPS spoofer,” in Proc of the International Technical Meeting of the Institute of Navigation, Anaheim, CA, USA, pp 124 – 130, Jan 2009 [9] S Pullen, G.X Gao, “GNSS Jamming in the name of Privacy,” in Inside GNSS, vol 7, no 2, Mar./Apr 2012 [10] A Jafarnia-Jahromi, A Broumandan, J Nielsen, and G Lachapelle, “GPS vulnerability to spoofing threats and a review of antispoofing techniques,” in the International Journal of Navigation and Observation, vol 2012, pp 1–16, May 2012 [11] A Konovaltsev, et al., "Autonomous Spoofing Detection and Mitigation in a GNSS Receiver with an Adaptive Antenna Array," in Proc of the 26th Int 128 Tech Meeting of the Sat Division of The Institute of Navigation (ION GNSS 2013), Nashville, TN, September 2013, pp 2937-2948 [12] J Merrill, “Patriot Watch: Vigilance Safeguarding America,” presented at the Presentation Telcordia-NIST-ATIS Workshop Synchronization Telecommun Syst (WSTS ’12), Mar 20–22, 2012 [Online] Available: https://www.gps.gov/multimedia/presentations/2012/03/WSTS/merrill.pdf , [Accessed: 17-Feb-2021] [13] D Borio and C Gioia, “A sum-of-squares approach to GNSS spoofng detection”, IEEE Trans on Aerospace and Electronic Systems, Vol 52, No 4, pp 1756–1768, 2016 [14] Li, Bowen, et al "An improved model and simulator design of GNSS ocean reflected signals." 2017 Forum on Cooperative Positioning and Service (CPGPS) IEEE, 2017 [15] National Defense Magazine: https://www.nationaldefensemagazine.org [16] “Resilient Navigation and Timing Foundation”: https://rntfnd.org/ [Last visited Nov 17, 2020] [17] Inside GNSS magazine: https://insidegnss.com/ [Last visited Nov 17, 2020] [18] GEOSpatial World: https://www.geospatialworld.net/ [Last visited Nov 17, 2020] [19] M Troglia Gamba, D M Truong, B Motella, E Falletti, T H Ta, “Hypothesis testing methods to detect spoofing attacks: a test against the TEXBAT datasets,” in GPS Solutions, vol 21 (2), June 2016 [20] K.D Wesson,, D.P Shepard, J.A Bhatti, and T.E Humphreys, "An Evaluation of the Vestigial Signal Defense for Civil GPS Anti-Spoofing," in Proc of ION GNSS 2011, Portland, Oregon, USA, Sept 2011 [21] S C Lo and P K Enge, “Authenticating aviation augmentation system broadcasts,” in Proc of the IEEE/ION Position, Location and Navigation Symposium (PLANS '10), pp 708–717, Indian Wells, CA, USA, May 2010 [22] C E McDowell, “GPS Spoofer and Repeater Mitigation System using Digital Spatial Nulling—US Patent 7250903 B1,” 2007 [23] J Nielsen, A Broumandan, and G Lachapelle, “Spoofing detection and mitigation with a moving handheld receiver,” GPS World, vol 21, no 9, pp 27–33, 2010 [24] J Magiera, R Katulski, “Accuracy of differential phase delay estimation for GPS spoofing detection,” in Proceedings of the 36th international conference on telecommunications and signal processing, September 2013, pp 695–699 doi:10.1109/TSP.2013.6614026 129 [25] P Y Montgomery, T E Humphreys, B M Ledvina, “Autonomous spoofing detection: experimental results of a multiantenna receiver defense against a portable civil GPS spoofer,” in Proceedings of the ITM 2009 Institute of Navigation, Anaheim, CA, January 2009, pp 124–130 [26] D Borio and C Gioia “A dual-antenna spoofing detection system using GNSS commercial receivers”, in Proc of ION GNSS+ 2015, Tampa, FL, USA, Sep 2015, pp.1–6 [27] T E Humphreys, B M Ledvina., M L Psiaki, B W O’ Hanlon, and P M Kintner, Jr, “Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer,” in Proc of ION GNSS 2008 of the Institute of Navigation, Savanna, GA, USA, Sept 2008 [28] R.T Ioannides, T Pany, G Gibbons, “Known Vulnerabilities of Global Navigation Satellite Systems, Status, and Potential Mitigation Techniques,” in the Proceedings of the IEEE, vol 104, no 6, pp 1174-1194, June 2016 [29] L Canzian et al., “Interference localization from space Theoretical background,” in Inside GNSS, November/December 2016, pp 59-68 [30] Broumandan, Ali, Ali Jafarnia-Jahromi, and Gérard Lachapelle "Spoofing detection, classification and cancelation (SDCC) receiver architecture for a moving GNSS receiver." Gps Solutions 19.3 (2015): 475-487 [31] Geng, Z., Huang, Y., Chen, H., & Wang, F (2018) “GNSS Spoofing Mitigation Method After Despreading.” China Satellite Navigation Conference (CSNC) 2018 Proceedings, 423–434.doi:10.1007/978-981-130029-5_37 [32] D Margaria, B Motella, M Anghileri, J J Floch, I Fernandez-Hernandez and M Paonni, “Signal Structure-Based Authentication for Civil GNSSs: Recent Solutions and Perspectives,” in IEEE Signal Proc Magazine, vol 34, no 5, pp 27-37, Sep 2017 doi: 10.1109/MSP.2017.2715898 [33] Fabio.Dovis, “Recent trends in Interference Mitigation and Spoofing Detection ” ICL-GNSS 2011, Tampere, 30/06/2011 [34] https://gssc.esa.int/navipedia/index.php/CDMA_FDMA_Techniques [Last visited Nov 17, 2020] [35] Fina Otosi Faithpraise, Effiong Okokon Obisung, Joseph Offiong “The Design and Use of Dual Modules System for Domestic Animals Monitoring (DMS)” International Journal Of Environmental & Science Education E-ISSN: 13063065 2019, Vol 14, No 5, 311-323 130 [36] https://gssc.esa.int/navipedia/index.php/GNSS_signal [Last visited Nov 17, 2020] [37] Gross, Jason, and Todd E Humphreys "GNSS spoofing, jamming, and multipath interference classification using a maximum-likelihood multi-tap multipath estimator." Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, CA, USA 2017 [38] http://www.celljammerstore.com/gps-jammers.html [Last visited Nov 17, 2020] [39] Wesson, K., Rothlisberger, M., and Humphreys, T “Practical cryptographic civil gps signal authentication.” NAVIGATION, Journal of the Institute of Navigation, 59, (2012), 177–193 [40] Scott, L “Anti-spoofing and authenticated signal architectures for civil navigation systems.” In Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS), Portland, OR, Sep 2003, 1543–1552 [41] Broumandan, A., Jafarnia-Jahromi, A., Dehghanian, V., Nielsen, J., and Lachapelle, G “GNSS spoofing detection in handheld receivers based on signal spatial correlation.” In Proceedings of the IEEE/ION Position Location and Navigation Symposium (PLANS), Apr 2012, 479–487 [42] E Falletti, B Motella and M T Gamba, "Post-correlation signal analysis to detect spoofing attacks in GNSS receivers", Proc 24th Eur Signal Process Conf (EUSIPCO), pp 1048-1052, Aug./Sep 2016 [43] D M Akos, “Who's afraid of the spoofer? GPS/GNSS spoofing detection via automatic gain control (AGC)” in NAVIGATION, Journal of the Institute of Navigation, Vol 59, No 4, pp 281– 290, 2012 [44] M L Psiaki and T E Humphreys, “GNSS spoofing and detection,” in Proc of the IEEE, vol 104, no 6, pp 1258–1270, June 2016 [45] Psiaki, M L., O’Hanlon, B W., Powell, S P., Bhatti, J A., Wesson, K D., Humphreys, T E., and Schofield, A “GNSS spoofing detection using twoantenna differential carrier phase.” In Proceedings of the 27th International 131 Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS + ), Tampa, FL, Sep 2014, 2776–2800 [46] “Fundamentals of Global Positioning System Receivers:A Software Approach” January 2005 DOI:10.1002/0471712582 Edition: 2ndPublisher: John Willey & Sons, Inc James Bao Yen Tsui [47] https://cddis.nasa.gov/archive/gnss/data/daily [Last visited Nov 17, 2020] [48] “A Software-Defined GPS and Galileo Receiver A Single-Frequency Approach” 2007 Authors: Borre, K., Akos, D.M., Bertelsen, N., Rinder, P., Jensen, S.H DOI 10.1007/978-0-8176-4540-3 [49] P Cederholm, and D Plausinaitis, “Cycle Slip Detection in Single Frequency GPS Carrier observations using expected Doppler shift”, Nordic Journal of Surveying and Real Estate Research (2014) [50] F De Ponte Müller, A Steingass, and T Strang, "Zero-Baseline Measurements for Relative Positioning in Vehicular Environments," in Proceedings of the 6th European Workshop on GNSS Signals and Signal Processing, 2013 [51] Septentrio AsteRx4 OEM website: https://www.septentrio.com/products/gnss-receivers/rover-basereceivers/oem-receiver-boards/asterx4-oem [52] IFEN NavX-NCS Essential Simulator website: https://www.ifen.com/products/navx-ncs-essential-gnss-simulator/ [53] M K Simon, “Probability Distributions Involving Gaussian Random Variables,” New York: Springer, 2002, eq (2.35), ISBN 978-0-387-34657-1 [54] M Abramowitz, and I A Stegun, “Handbook of Mathematical Functions,” 10th Ed Dover, 1972 Online: http://people.math.sfu.ca/~cbm/aands/ [55] J M Borwein, and I J Zucker, "Fast Evaluation of the Gamma Function for Small Rational Fractions Using Complete Elliptic Integrals of the First Kind" IMA J Numerical Analysis doi:10.1093/imanum/12.4.519 132 12 (4): 519–526, 1992 [56] J N L Johnson, S Kotz, and N Balakrishnan, “Chi-Square Distributions including Chi and Rayleigh Continuous Univariate Distributions.” (Second ed.) John Wiley and Sons pp 415–493, 1994, ISBN 978-0-471-58495-7 [57] R W Abernathy, and R P Smith, "Applying series expansion to the inverse beta distribution to find percentiles of the F-distribution", in ACM Transactions on Mathematical Software, Vol 9, No.4, pp 478–480, 1994 [58] S Lo, Y.H Chen, H Jain, P Enge, "Robust GNSS Spoof Detection using Direction of Arrival: Methods and Practice," in Proc of the 31st Int Tech Meeting of the Sat Division of The Institute of Navigation (ION GNSS 2018), Miami, Florida, September 2018, pp 2891-2906 [59] C Jiang, et al.,"Analysis of the baseline data based GPS spoofing detection algorithm," in Proc of IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, 2018, pp 397-403 [60] “GNSS raw data in the presence of spoofing”: Zenodo Link: https://zenodo.org/record/2537055#.XjvtmPlKi70 [Last visited Feb 6, 2020] [61] I Barrodale and F D K Roberts, "An improved algorithm for discrete L1 linear approximation" SIAM Journal on Numerical Analysis 10 (5), 1973, pp: 839–848 doi:10.1137/0710069 [62] M.G Akritas, S.A Murphy, and M.P LaValley, "The Theil-Sen estimator with doubly censored data and applications to astronomy", Journal of the American Statistical Association, 90, 1995, pp: 170–177, doi:10.1080/01621459.1995.10476499 [63] A.C Jensen, "Deming regression, MethComp package", 2007 [64] S Raj and S Kannan, “Detection of Outliers in Regression Model for Medical Data”, International Journal of Medical Research & Health Sciences, 6(7), 2017, pp: 50-56 ISSN No: 2319-5886 [65] Y Huang, K Englehart, B Hudgins, A.D.C Chan, “A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses”, IEEE Trans Biomed Eng., 52 (11) (2005), pp 1801-1811 133 [66] Amruthnath and Gupta, 2018 N Amruthnath, T Gupta, “ A research study on unsupervised machine learning algorithms for fault detection” in predictive maintenance 5th InternationalEr conference on industrial engineering and applications (ICIEA), IEEE (2018), pp 355-361 [67] Christopher M Bishop F.R.Eng (2006), “Pattern Recognition and Machine Learning”, Springer Science Business Media, LLC [68] Sultan Alzahrani (2021) “Expectation Maximization Algorithm.zip” (https://www.mathworks.com/matlabcentral/fileexchange/47889expectation-maximization-algorithm-zip), MATLAB Central File Exchange Retrieved September 12, 2021 [69] Tech rep., John A Volpe “Vulnerability assessment of the transportation infrastructure relying on the Global Positioning System,” National Transportation Systems Center, 2001 [70] Rui Xu, Mengyu Ding, Ya Qi, Shuai Yue, Jianye Liu, “Performance Analysis of GNSS/INS Loosely Coupled Integration Systems under Spoofing Attacks” Published in Sensors 2018 DOI:10.3390/s18124108 [71] Y.F.Hu, S.F Bian, B Ji, J Li, “GNSS spoofing detection technique using fraction parts of double-difference carrier phases”, J Navig 2018, 71, 1111– 1129 [72] Li He, Hong Li, Mingquan Lu, “Dual-antenna GNSS spoofing detection method based on Doppler frequency difference of arrival”, GPS Solutions July 2019 [73] Esteban Garbin Manfredini, Dennis M Akos, Yu-Hsuan Chen, Sherman Lo, Todd Walter, and Per Enge, “Effective GPS Spoofing Detection Utilizing Metrics from Commercial Receivers,” Proceedings of the Institute of Navigation International Technical Meeting, Reston, VA January 2018 [74] G Caparra, J.T Curran, “On the Achievable Equivalent Security of GNSS Ranging Code Encryption” in IEEE/ION Position, Location and Navigation Symposium (PLANS) 2018, (Monterey, California), 2018 134 [75] Thuan, Nguyen Dinh, Ta Hai Tung, and Lo Presti Letizia "A software based multi-IF output simulator." Proceedings of the International Symposium of GNSS (IS-GNSS), Kyoto, Japan 2015 [76] Falletti, Emanuela, Marco Pini, and L Lo Presti "Are carrier-to-noise algorithms equivalent in all situations." Inside GNSS 2010 (2010): 20-27 135