Research and development of advanced signal processing algorithms for multi GNSS software receivers (nghiên cứu và phát triển giải thuật xử lý tín hiệu tiên tiến cho bộ thu mềm đa

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY - TRUONG MINH DUC RESEARCH AND DEVELOPMENT OF ADVANCED SIGNAL PROCESSING ALGORITHMS FOR MULTI-GNSS SOFTWARE RECEIVERS MASTER OF SCIENCE THESIS COMPUTER AND COMMUNICATION ENGINEERING ACADEMIC SUPERVISOR: Dr Tạ Hải Tùng Hanoi – 2015 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI - TRƯƠNG MINH ĐỨC NGHIÊN CỨU VÀ PHÁT TRIỂN GIẢI THUẬT XỬ LÝ TÍN HIỆU TIÊN TIẾN CHO BỘ THU MỀM ĐA HỆ THỐNG GNSS Chuyên ngành : Kỹ thuật máy tính truyền thông LUẬN VĂN THẠC SĨ KHOA HỌC KỸ THUẬT MÁY TÍNH VÀ TRUYỀN THÔNG NGƯỜI HƯỚNG DẪN KHOA HỌC : TS Tạ Hải Tùng Hà Nội – Năm 2015 Acknowledgements In the first words of this thesis, I am extremely grateful to those who in various ways contributed to all the research activities presented in this thesis Foremost, I would like to express my sincere gratitude to my advisor Dr Ta Hai Tung for the continuous support, for his patience, motivation, enthusiasm, and immense knowledge His guidance helped me in all the time of research and writing of this thesis I could not have imagined having a better advisor and mentor I deeply acknowledge the supports from all the NAVIS and ISMB members, who everyday show me the intelligence, kindness and hospitality Among them, I would like to mention Micaela Troglia Gamba, Emanuela Falletti, Nguyen Dinh Thuan, Nguyen Thi Thanh Tu whose contributions from the very beginning of my research are very important The list could not be complete without Growing NAVIS project, funded by the European Commission under the FP7 Call Galileo.2011.4.3-1 – International Activities (Grant Agreement No 287203), for supporting my internship at ISMB from March to June, 2014 Last but very not least, I cannot thank enough my parents and my sister for their belief, encouragement and love that definitely are my limitless power Commitment I commit myself to be the person who was responsible for conducting this study All reference figures were extracted with clear derivation The presented results are truthful and have not published in any other person‟s work HaNoi, December 29th, 2014 Trương Minh Đức TÓM TẮT LUẬN VĂN Ngày nay, định vị sử dụng vệ tinh đóng vai trò quan trọng nhiều lĩnh vực: giao thông, đồ, cứu hộ, giám sát môi trường, quân sự,… Các hệ thống định vị sử dụng vệ tinh đại bao gồm hệ thống cũ nâng cấp GPS, GLONASS hay hệ thống xây dựng Galileo, Beido cung cấp tín hiệu với công nghệ tiên tiến như: điều chế dịch sóng mang nhị phân (BOC), kỹ thuật ghép kênh điều chế sóng mang thích nghi tương quan (CASM)… Một yêu cầu đặt cho thu tín hiệu phải có khả hoạt động với tín hiệu mới, hoạt động đa hệ thống Tuy nhiên, để đáp ứng yêu cầu này, thu cứng truyền thống (ASIC) cần phải thiết kế chế tạo lại Điều yêu cầu chi phí tương đối cao Do vậy, hướng phát triển thu mềm hoạt động vi xử lý có khả lập trình được quan tâm rộng rãi với phát triển mạnh mẽ lực tính toán vi xử lý Bộ thu mềm có ưu điểm cấu trúc xử lý linh hoạt, mềm dẻo dễ dàng thực việc nâng cấp, thay đổi hoàn toàn đáp ứng yêu cầu Ngoài ra, nhiều ứng dụng định vị sử dụng vệ tinh có yêu cầu cao độ an toàn tính xác hàng hải, hàng không, hay đường sắt Hiện nay, mối đe dọa tới độ xác dịch vụ định vị sử dụng vệ tinh can nhiễu phá sóng hay giả mạo tín hiệu Tuy nhiên, tín hiệu GNSS phổ thông không trang bị phương pháp chống can nhiễu bên Vì vậy, phương pháp chống can nhiễu trình xử lý tín hiệu vấn đề cần xem xét thu Từ yêu cầu thực tế đó, luận văn tập trung vào nghiên cứu phát trình thu mềm đa hệ thống nghiên cứu phương pháp phát giả mạo tín hiệu Đóng góp luận văn sau:  Nghiên cứu phát triển thu mềm đa hệ thống, đánh giá hiệu giải pháp định vị đa hệ thống so với giải pháp đơn hệ thống sử dụng liệu thực  Nghiên cứu hai phương pháp phát giả mạo tín hiệu, với tên gọi SignTest GoF, đánh giá khả hoạt động hai phương pháp với liệu chuẩn TEXTBAT sử dụng rộng rãi đánh giá hiệu kỹ thuật phát hiện/ loại bỏ giả mạo tín hiệu Từ nội dung trên, luận văn tổ chức sau:  Chương 1: Fundamental Background: Chương giới thiệu tổng quan môi trường định vị đa hệ thống – kiến trúc bản, trạng thái hệ thống định vị sử dụng vệ tinh có, ưu điểm thách thức định vị đa hệ thống Cơ sở lý thuyết thu mềm giới thiệu chương  Chương 2: Design of Multi-GNSS receiver: Chương tập trung vào thiết kế thu mềm Cách triển khai module thu mềm phương thức hoạt động module trình bày chương  Chương 3: Spoofing detection on software receiver: Chương tổng hợp thông tin hai phương pháp phát giả mạo sử dụng thu mềm cách triển khai module Ngoài chương điểm qua số đặc điểm liệu chuẩn TEXTBAT  Chương 4: Experiment Results: Chương trình bày kết thử nghiệm hoạt động thu mềm với hệ thống định vị sử dụng vệ tinh toàn cầu Các kết thực nghiệm cho thấy giải pháp định vị đa hệ thống hoàn toàn thực giải pháp cho hiệu tốt giải pháp đị vị đơn hệ thống độ xác, khả sẵn sàng độ tin cậy Kết thử nghiệm hai phương pháp phát giả mạo tín hiệu trình bày chương Hai phương pháp cho thấy khả phát tín hiệu bị giả mạo áp dụng với liệu TEXTBAT  Kết luận Tóm lại, luận văn thực yêu cầu đặt ban đầu nghiên cứu phát triển thu mềm đa hệ thống, thử nghiệm hoạt động thu mềm; nghiên cứu, kiểm thử khả phát giả mạo hai phương pháp SignTest GoF Table of Contents Acknowledgements Commitment List of Acronyms List of Figures 11 List of Tables 12 Introduction 13 Chapter 1: Fundamental Background 16 Multi-GNSS environment 16 1.1 1.2 1.3 1.4 Architecture of GNSS 16 Status of all GNSSes 19 Advantages of multi-GNSS 22 Challenges of multi-GNSS 23 Software Receiver 24 2.1 2.2 Software receiver overview 24 Software receiver architecture 25 Chapter 2: Design of Multi-GNSS receiver 41 Receiver general architecture 41 Module implementation 42 2.1 2.2 2.3 2.4 2.5 Signal synchronization 42 Data demodulation 47 Satellite position computation 54 Pseudorange computation 56 PVT computation 57 Chapter 3: Spoofing detection on software receiver 62 Spoofing detection theory 62 1.1 1.2 1.3 1.4 Hypothesis test 62 Application of hypothesis test to GNSS receivers 63 Sign Test 65 Goodness of Fit Test 65 Spoofing detection method implementation 66 TEXTBAT datasets 70 Chapter 4: Experiment results 73 Graphic User Interface 74 Signal synchronization and Demodulation module 76 2.1 2.2 PVT Computation 79 3.1 3.2 Signal synchronization 76 Data demodulation 79 Single GNSS solution 81 Multi-GNSS solution 84 Spoofing detection 87 Conclusion 93 References 95 List of Acronyms AFS Atomic Frequency Standards AGNSS Assisted Global Navigation Satellite System AWGN Additive White Gaussian Noise BOC Binary Offset Carrier CDMA Code Division Multiple Access CPU Central Processing Unit DLL Delay Lock Loop DOP Dilution Of Precision DSP Digital signal processing ECEF Earth-Centered, Earth-Fixed EGNOS European Geostationary Navigation Overlay Service FDMA Frequency Division Multiple Access FEC Forward Error correction FFT Fast Fourier Transform FLL Frequency Lock Loop FPGA Field-programmable gate array GDOP Geometric Dilution Of Precision GEO Geostationary Earth Orbit GNSS Global Navigation Satellite System GoF Goodness of Fit GPS Global Positioning System GPU Graphics processing unit ICD Interface Control Document IF Intermediate Frequency IGSO Inclined geosynchronous orbit IRNSS Indian Regional Navigation Satellite System MEO Medium Earth orbit MSAS Multi-functional Satellite Augmentation System NCO Numerically controlled oscillator NDU Navigation Data Unit PDOP Position Dilution Of Precision PLL Phase Lock Loop PNT Positioning, Navigation and Timing PRN Pseudo-Random Noise PVT Position, Velocity, and Time QZSS Quasi-Zenith Satellite System RF Radio Frequency RNSS Regional Navigation Satellite System SDR Software Defined Radio TDOP Time Dilution Of Precision TEXTBAT Texas Spoofing Test Battery WAAS Wide Area Augmentation System 10 (a) (b) 82 (c) (d) Figure 42 Skyplot (left) and Positioning accuracy (right) of all GNSSes (a) Glonass (b) Beidou (c) Galileo (d) GPS Table shows the accuracy of the positioning results of each system obtained from the dataset of the campaign (with 200 fixes) Table Performance of stand-alone positioning solutions System δNorth (m) δEast (m) GDOP Glonass 3.2584 8.1746 3.3992 Beidou 3.7629 13.4952 3.5421 Galileo 4.0887 12.8882 3.7411 GPS 2.9859 6.3924 2.2609 The three parameters are location easting and northing errors (i.e standard deviation), and the Geometric Dilution of Precision (GDOP) parameter, an additional multiplicative effect of navigation satellite geometry on positioning results [12] When visible navigation satellites are close together in the sky, the geometry is said to be weak and the GDOP value is high; when far apart, the geometry is strong and the GDOP value is low As seen in Table 8, GPS has the highest positioning accuracy related to its GDOP being smallest The skyplot in Figure 42 shows the strong 83 geometry of GPS Meanwhile, Galileo shows its poor performance since at this moment there are only Galileo satellites, just enough for positioning, at low elevation angles Considering Beidou, its northing error is highest, since in Figure 42, its satellites are close together and of them are geostationary satellites at the common equatorial plane GLONASS gives a good accuracy since it is one of the two complete systems at this moment 3.2 Multi-GNSS solution This section focuses on the PVT result of multi-GNSS experiment, the acquisition and tracking result is still the same as the single-GNSS experiments We performed the analysis under some different scenarios; three of them are mentioned in this thesis: 3.2.1 GPS and Galileo In the first scenario, the receiver position is calculated by combining all available GPS satellites and Galileo satellites As seen in Table 1, the carrier frequency of GPS L1 C/A and Galileo E1 is the same (1575.42 MHz) So this combination is very easy to implement, we can receive signal from both systems with a GPS receiver antenna Figure 43 shows skyplot and the position accuracy of this scenario Figure 43 Skyplot & Positioning accuracy of {GPS & Galileo} combination The performance of this combination: 84  σNorth = 2.4029 m  σEast = 5.8056 m  GDOP = 1.96 The standard deviation and GDOP of the combination is a bit smaller than the standard deviation of GPS and significantly smaller than the standard deviation of Galileo (Table 8) So its accuracy is better than GPS and Galileo alone 3.2.2 Three GPS and two Beidou In the second scenario, we perform PVT calculation with GPS satellites and Beidou satellites This combination is very useful in some harsh cases such as urban canyon where signal from satellite is usually blocked by obstacles like tree, building… So it is possible that the receiver only gets the signal from GPS satellites and then it couldn‟t provide positioning services But we can solve that problem by combining GPS with Beidou As mentioned in chapter Section 1.2, Beidou constellation consists of GEO and IGSO satellites which are available in the sky most of time Then, with Beidou satellites and GPS satellites, we can perform PVT calculation as normal Figure 44 shows skyplot and the position accuracy of this combination Figure 44 Skyplot & Positioning accuracy of {3 GPS & Beidou} combination The performance of this combination is: 85  σNorth = 5.4983 m  σEast = 8.0544 m  GDOP = 4.2 This result has the east deviation greater than the east deviation of all standalone solution but its north deviation is still lower than the north deviation of Galileo and Beidou only solution (Table 8) The GDOP value of the combination is about 4.2 Although it is higher than GDOP of standalone solutions, it is still lower than [23] Therefore, by using multi-GNSS solution, we can increase the availability of the positioning services in the harsh cases while the accuracy and precision are still acceptable 3.2.3 All GNSS systems In the last scenario, we combine all possible satellites of all four systems to perform PVT calculation Skyplot and the position accuracy of this combination are presented on Figure 45 Figure 45 Skyplot & Positioning accuracy of {four systems} combination The performance of all systems combination:  σNorth = 1.7582 m 86  σEast = 3.7840 m  GDOP = 1.89 The standard deviation of combination solution is smaller than the standard deviation of single-GNSS solution The ratio between the deviation of this combination and single-GNSS solution is given in Table Table Standard deviation ratio System North ratio East ratio Glonass 1.85 2.16 Beidou 2.14 3.57 Galileo 2.33 3.41 GPS 1.7 1.69 As we can see in Table 9, the accuracy of the combination solutions is much better than the single-GNSS solutions (1.7 times better than GPS, 2.8 times better than Beidou and Galileo, times better than Glonass) The GDOP of combination solution is also smaller than any other solutions So, by combining more than one system to a multi-GNSS solution, we can improve the accuracy and precision of the positioning services Spoofing detection The results in this section were presented in [21] The results presented hereafter refer to the processing of the Scenario dataset of the TEXTBAT dataset (see Section 3.2 chapter 1), referred to as ‘Static Matched-Power Time Push’ in [10] Such a name indicates the main characteristics of the attack: the scenario is static, the spoofing and the real signal‟s power levels match (i.e., the power advantage of the spoofer is less than dB), and the spoofer induces an error along the time dimension (i.e., an offset of μs, equivalent to 600 meters) 87 As expected from [10], all the satellites in lock present variations in the amplitude: Figure 46 shows the trend of carrier to noise ratio for four PRNs (3, 13, 16, and 23), clearly highlighting the critical time interval between 100 and 270 seconds Such fluctuations are due to the existing residual differential Doppler in the spoofing signal, as explained in [10] In fact, the imprecise frequency lock of the spoofer to the Doppler shift causes the counterfeit and real phasors to slowly rotate with respect to each other, determining a power leakage from in-phase to quadrature, and a consequent loss of estimate [7] 60 55 CN0 [dB-Hz] 50 45 40 PRN PRN 13 PRN 16 PRN 23 35 30 50 100 150 200 250 Time [s] 300 350 400 Figure 46 Time history of C/N0 for different PRNs The Doppler profile is plotted in Figure 47 for the four satellites analyzed in Figure 46 Differently from the receiver output taken as reference in [10] (see Figure 14), our receiver, though it is able to maintain the lock of almost all the satellites, presents some imperfections at the tracking stage, pointed out by the irregularities in the Doppler profiles They are likely related to the phasors‟ rotation that stimulates the tracking loops 88 fD [Hz] 1000 500 100 200 300 400 fD [Hz] 2000 PRN 13 1800 1600 100 200 300 400 -2700 PRN 16 -2800 D f [Hz] PRN fD [Hz] -2900 100 200 300 400 200 Time [s] 300 400 -400 -600 -800 PRN 23 100 Figure 47 Doppler frequency fD of different PRNs The results on the position output are in line with those reported by [10] (Figure 15): substantial errors are presented in the time interval in which the „time push‟ is applied ([150÷270] seconds, according to Figure 16 of [10]), while in the last part of the dataset the position error gets back to its nominal value At the receiver side, it might not be straightforward to realize to be under a spoofing attack For this purpose, the two methods, namely the GoF and the Sign Test, have been applied at the correlators‟ output The results are summarized for the GoF and the Sign Test in Figure 48 and Figure 49 respectively Each figure contains four plots, one per PRN (3, 13, 16, 23, and 23) Each plot is composed by two graphs:  the trend of the p-value for the clean (grey lines) and spoofed (blue lines) datasets, and the relative threshold α (red lines);  the results of the hypothesis test H on the spoofed dataset, i.e.:  H  (H rejected) p-value   if  then  p-value   H  (H accepted) 89 GPS PRN Sign Test, T =1 ms, d =1.5 chip 10 10 10 GPS PRN 13 Sign Test, T =1 ms, d =1.5 chip EL 10 -2 10 -4 p-value p-value 10 int -6 -8 -10 10 10 100 150 50 100 150 200 250 300 350 400 200 250 Time [s] 300 350 400 -4 -6 -8 10 1.5 0.5 -0.5 Clean Spoofed  50 100 150 50 100 150 (a) 10 10 10 int 10 -2 10 -4 -6 -8 -10 Clean Spoofed  10 10 10 300 350 400 200 250 Time [s] 300 350 400 int EL -2 -4 -6 -8 Clean Spoofed  -10 50 100 150 50 100 150 200 250 300 350 400 200 250 Time [s] 300 350 400 10 1.5 0.5 -0.5 50 100 150 50 100 150 200 250 300 350 400 200 250 Time [s] 300 350 400 H H 10 1.5 0.5 -0.5 250 GPS PRN 23 Sign Test, T =1 ms, d =1.5 chip EL p-value p-value 10 200 (b) GPS PRN 16 Sign Test, T =1 ms, d =1.5 chip 10 EL -2 -10 50 H 10 1.5 0.5 -0.5 Clean Spoofed  10 int H 10 (c) (d) Figure 48 Results of Sign Test applied to a pair of correlators with dEL=1.5chips and integration period Tint = 1ms (a) GPS PRN (b) GPS PRN 13 (c) GPS PRN 16 (d) GPS PRN 23 The threshold α has been set during the processing of the clean datasets (calibration phase [8]) and fixed to 10-6 and 10-4 for the Sign and the GoF test respectively The first 100 seconds of dataset are clean and the hypothesis on the absence of distortion is always accepted (in both tests, Figure 48 and Figure 49), i.e., H is always 90 equal to 0, unless for a single case of false alarm, when the GoF is applied to the correlators of PRN 3, Figure 49(a) GPS PRN 13 GoF, T =1 ms, d =1.5 chip GPS PRN GoF, T =1 ms, d =1.5 chip 10 EL 10 -2 10 p-value -4 -6 10 1.5 0.5 -0.5 10 Clean Spoofed  -4 100 150 50 100 150 200 250 300 350 400 200 250 Time [s] 300 350 400 10 1.5 0.5 -0.5 Clean Spoofed  50 100 150 50 100 150 (a) 250 300 350 400 200 250 Time [s] 300 350 400 GPS PRN 23 GoF, T =1 ms, d =1.5 chip EL 10 -2 int EL -2 p-value 10 p-value 10 int 200 (b) GPS PRN 16 GoF, T =1 ms, d =1.5 chip 10 -4 -6 10 1.5 0.5 -0.5 10 -4 Clean Spoofed  Clean Spoofed  -6 50 100 150 50 100 150 200 250 300 350 400 200 250 Time [s] 300 350 400 H 10 H EL -2 -6 50 H 10 H int p-value 10 int 10 1.5 0.5 -0.5 50 100 150 50 100 150 (c) 200 250 300 350 400 200 250 Time [s] 300 350 400 (d) Figure 49 Results of GoF test applied to a pair of correlators with dEL=1.5chips and integration period Tint = 1ms (a) GPS PRN (b) GPS PRN 13 (c) GPS PRN 16 (d) GPS PRN 23 In the rest of the dataset (after second 120 and up to the end of the data) all the correlation functions of the four PRNs present anomalies detectable by the two statistical tests Both methods are in fact able to the rise an alert on the possible 91 presence of undesired signals during the spoofing attack, i.e., there are many detection events (i.e., H=1) for all the observed PRNs From the result, we also can see that GoF has a bit better performance than SignTest since GoF gives more detection event than SignTest with some PRNs The same analysis has been conducted on dataset no obtaining very similar results The two methods are in fact able to discriminate between the absence and the presence of disturbances 92 Conclusion Software receiver is a new research direction in developing GNSS receiver Different with the hardware receiver approach, this approach allows building flexible GNSS receivers, easy to update, upgrade So these receivers can work in multi-GNSS environment and also easily implement and test new advanced signal processing algorithms Following this approach, the thesis focuses on developing a multi-GNSS receiver and testing the performance of the multi-GNSS solution Through the analysis, design, and the result present in the thesis, the initial objectives of the thesis are completed Specifically, the thesis has achieved the following:  Research on multi-GNSS environment: general architecture of GNSS system, status of current GNSS systems, advantages and challenges of multi-GNSS environment  Research on software receiver: sign processing chain, algorithms employed in operating stages of the receiver  Research on two spoofing detection method based on statistical testing – SignTest and GoF test  Analysis, design and implementation of a whole software receiver and also the two spoofing detection method mentioned above  Testing the receiver with real data collected and also proving that the multiGNSS positioning is possible and its performance is outperform stand-alone ones in terms of positioning accuracy, availability and reliability  Validating the spoofing detection methods on real data (TEXTBAT datasets) and prove their detection capability As a consequence, the focus for future works: 93  Exhaustive investigations of multi-GNSS positioning towards an objective of proposing suitable system combinations in different scenarios  Analysis of the other TEXTBAT datasets, to understand on the one hand the response of the detection method in case of overpowered attack with sudden pulloff of the true signal (Scenarios and 2), on the other hand to calibrate the methods to a dynamic scenario, in which the natural environmental effects 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