地面无人系统反制关键技术分析与综述

doi:10.7527/S1000-6893.2021.25489

Abstract

Abstract: Aiming at the future needs of unmanned and intelligent equipment countermeasures, summarizing the capability characteristics and key technologies of unmanned ground systems by sorting out it's current situation and trends of the development at home and abroad, and comparing and analyzing with unmanned aerial systems, the possible strategies for countermeasures against unmanned ground systems are given. The key technology system is initially constructed, and the relevant key technologies are analyzed and reviewed from the aspects of countermeasure Tracking Telemetering and Command(TT&C) data links, satellite navigation and positioning systems, environment sensing sensors, autonomous behavior and platform control and online assessment of countermeasure effects, which will be of inspiration and reference for future equipment construction and development, and play a role in promoting the development and capability enhancement of unmanned ground equipment.

Key words: Unmanned Ground System (UGS), countermeasure, Tracking Telemetering and Command (TT&C) data link, navigation position, environment sensing sensorhttp

CLC Number:

V19
TN97

WANG Wei, WANG Qinzhao, LIU Gangfeng, CHENG Hui, TAO Yi, GUO Aobing. Countering unmanned ground system:A review of key technologies[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(7): 25489.

References

[1] 薛春祥,黄孝鹏,朱咸军,等.外军无人系统现状与发展趋势[J].雷达与对抗, 2016, 36(1):1-5, 10. XUE C X, HUANG X P, ZHU X J, et al. Status quo and development trends of foreign military's unmanned systems[J]. Radar&ECM, 2016, 36(1):1-5, 10(in Chinese).
[2] 孟红,朱森.地面无人系统的发展及未来趋势[J].兵工学报, 2014, 35(S1):1-7. MENG H, ZHU S. The development and future trends of unmanned ground systems[J]. Acta Armamentarii, 2014, 35(Sup 1):1-7(in Chinese).
[3] SHIMOMURA N, FUJIMOTO K, OKI T, et al. An algorithm for distinguishing the types of objects on the road using laser radar and vision[J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(3):189-195.
[4] DÍAZ R, HALLMAN S, FOWLKES C C. Detecting dynamic objects with multi-view background subtraction[C]//2013 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2013:273-280.
[5] MORO A, MUMOLO E, NOLICH M, et al. Real-time background modeling based on classified dynamic objects for human robot application[C]//2012 Ninth International Conference on Networked Sensing (INSS). Piscataway:IEEE Press, 2012:1-7.
[6] 赵万龙,孟维晓,韩帅.多源融合导航技术综述[J].遥测遥控, 2016, 37(6):54-60. ZHAO W L, MENG W X, HAN S. A survey of multi-source information fusion navigation[J]. Journal of Telemetry, Tracking and Command, 2016, 37(6):54-60(in Chinese).
[7] 刘严岩,王进,冒蓉.无人地面车辆的环境感知技术[J].太赫兹科学与电子信息学报, 2015, 13(5):810-815. LIU Y Y, WANG J, MAO R. Environment perception technology of unmanned ground vehicles[J]. Journal of Terahertz Science and Electronic Information Technology, 2015, 13(5):810-815(in Chinese).
[8] 王杰东,刘北,董强健.美国防部《无人系统综合路线图》分析[J].飞航导弹, 2019(5):30-33. WANG J D, LIU B, DONG Q J. Analysis of American DAP-AR'Unmanned Systems Integrated Roadmap'[J]. Aerodynamic Missile Journal, 2019(5):30-33(in Chinese).
[9] Unmanned systems integrated roadmap FY2017-2042[EB/OL]. http://cdn.defensedaily.com/wp-content/uploads/post_attachment/206477.pdf, 2018.
[10] 芦鹏.基于高光谱成像技术的运动人脸姿态识别方法[J].激光杂志, 2020, 41(11):105-109. LU P. Moving face gesture recognition method based on hyper-spectrum imaging technology[J]. Laser Journal, 2020, 41(11):105-109(in Chinese).
[11] XIE Z H, NIU J Y, YI L, et al. Regularization and attention feature distillation base on light CNN for hyperspectral face recognition[J]. Multimedia Tools and Applications, 2021:1-17.
[12] 雷帆朴.基于位敏阳极的单光子成像关键技术研究[D].西安:中国科学院大学(中国科学院西安光学精密机械研究所), 2019. LEI F P. Research on the key technology of single photon imaging based on position sensitive anode[D]. Xi'an:Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, 2019(in Chinese).
[13] KONG L D, ZHAO Q Y, ZHENG K, et al. Noise-tolerant single-photon imaging with a superconducting nanowire camera[J]. Optics Letters, 2020, 45(24):6732-6735.
[14] 高博,张乃千,范旭.反无人机电子战发展[J].国防科技, 2019, 40(1):35-39. GAO B, ZHANG N Q, FAN X. Analysis on the development and application of Anti-UAV electronic warfare[J]. National Defense Technology, 2019, 40(1):35-39(in Chinese).
[15] LU T T, ZHANG M, KANG T B, et al. Joint TOA/DOA estimation using the SAGE algorithm in OFDM systems with virtual carriers[J]. Journal of Physics:Conference Series, 2019, 1169:012057.
[16] GOUDA M, ADAMS E R, HILL P C J. Detection&discrimination of covert DS/SS signals using triple correlation[C]//Proceedings of the Fifteenth National Radio Science Conference. Piscataway:IEEE Press, 1998:C35/1-C35/6.
[17] SHI S, ZHANG T Q, XU X, et al. Detection of DSSS/UQPSK signal based on fourth-order cumulant algorithm[C]//20147th International Congress on Image and Signal Processing. Piscataway:IEEE Press, 2014:1038-1042.
[18] JIN Y, JI H B. A cyclic-cumulant based method for DS-SS signal detection and parameter estimation[C]//2005 IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. Piscataway:IEEE Press, 2005:966-969.
[19] FEI Z, MING L, BIN T. Blind detection of DS/SS signals through variance fractal dimension trajectory[C]//2008 International Conference on Computer and Electrical Engineering. Piscataway:IEEE Press, 2008:145-149.
[20] UYSAL C, FILIK T. Presence detection of long-and-short-code DS-SS signals using the phase linearity of multichannel sensors[C]//201419th International Conference on Digital Signal Processing. Piscataway:IEEE Press, 2014:305-309.
[21] 刘万贤,彭华.一种突发直扩信号盲检测算法[J].信息工程大学学报, 2013, 14(6):700-707. LIU W X, PENG H. Algorithm for blind detection of the burst DSSS signal[J]. Journal of Information Engineering University, 2013, 14(6):700-707(in Chinese).
[22] 欧阳鑫信,姚山峰,杨宇翔,等.跳频信号的相参与非相参积累时频差估计方法[J].系统工程与电子技术, 2021, 43(5):1184-1190. OUYANG X X, YAO S F, YANG Y X, et al. Coherent and non-coherent integration TDOA/FDOA estimation method of frequency-hopping signals[J]. Systems Engineering and Electronics, 2021, 43(5):1184-1190(in Chinese).
[23] O'SHEA T J, CORGAN J, CLANCY T C. Convolutional radio modulation recognition networks[C]//Engineering Applications of Neural Networks, 2016.
[24] O'SHEA T J, CORGAN J, CLANCY T C. Unsupervised representation learning of structured radio communication signals[C]//2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE). Piscataway:IEEE Press, 2016:1-5.
[25] O'SHEA T J, WEST N, VONDAL M, et al. Semi-supervised radio signal identification[C]//201719th International Conference on Advanced Communication Technology (ICACT). Piscataway:IEEE Press, 2017:33-38.
[26] O'SHEA T, WEST N. Radio machine learning dataset generation with GNU radio[C]//Proceedings of the GNU Radio Conference, 2016:1-6.
[27] 杨司韩,金山,彭华,等.基于谱图和深度卷积网络的超短波特定信号检测和识别方法[J].信息工程大学学报, 2019, 20(2):129-135. YANG S H, JIN S, PENG H, et al. Ultra-short wave specific signal detection and recognition based on spectrogram and deep convolution neural network[J]. Journal of Information Engineering University, 2019, 20(2):129-135(in Chinese).
[28] 孙伟,彭华,李天昀,等.基于时频图像处理的宽带特定信号检测方法[J].信息工程大学学报, 2020, 21(5):545-551. SUN W, PENG H, LI T Y, et al. Broadband specific signal detection method based on time-frequency image processing[J]. Journal of Information Engineering University, 2020, 21(5):545-551(in Chinese).
[29] ZHENG Y L, GONG Q Y, ZHANG S F. Time-frequency feature-based underwater target detection with deep neural network in shallow sea[J]. Journal of Physics:Conference Series, 2021, 1756(1):012006.
[30] 李知达.基于SIFT算法的激光成像雷达点云图像与可见光图像的融合研究[D].西安:西安电子科技大学, 2014. LI Z D. A fusion algorithm of the images from the LADAR and the camera based on SIFT[D]. Xi'an:Xidian University, 2014(in Chinese).
[31] 曾湘峰.车载多传感器融合下的动态目标检测与跟踪[D].长沙:国防科学技术大学, 2015. ZENG X F. Dynamic object detection and tracking with vehicle-mounted multi-sensor fusion[D]. Changsha:National University of Defense Technology, 2015(in Chinese).
[32] 汪勇,张英,廖如超,等.基于可见光、热红外及激光雷达传感的无人机图像融合方法[J].激光杂志, 2020, 41(2):141-145. WANG Y, ZHANG Y, LIAO R C, et al. UAV image fusion method based on visible light, infrared light and lidar sensors[J]. Laser Journal, 2020, 41(2):141-145(in Chinese).
[33] WANG D, HU A Q, CHEN Y F, et al. An ESPRIT-based approach for RF fingerprint estimation in multi-antenna OFDM systems[J]. IEEE Wireless Communications Letters, 2017, 6(6):702-705.
[34] WANG S H, JIANG H L, FANG X F, et al. Radio frequency fingerprint identification based on deep complex residual network[J]. IEEE Access, 2020, 8:204417-204424.
[35] ZHUO F, HUANG Y L, CHEN J. Radio frequency fingerprint extraction of radio emitter based on I/Q imbalance[J]. Procedia Computer Science, 2017, 107:472-477.
[36] 刘高辉,张晓博.一种基于深度置信网络的通信辐射源个体识别方法[J].电波科学学报, 2020, 35(3):395-403. LIU G H, ZHANG X B. A method for personal identification of communication radiation source based on deep belief network[J]. Chinese Journal of Radio Science, 2020, 35(3):395-403(in Chinese).
[37] 周鑫,何晓新,郑昌文.基于图像深度学习的无线电信号识别[J].通信学报, 2019, 40(7):114-125. ZHOU X, HE X X, ZHENG C W. Radio signal recognition based on image deep learning[J]. Journal on Communications, 2019, 40(7):114-125(in Chinese).
[38] 陶冠宏,周林.一种基于深度学习的辐射源信号调制识别新算法[J].科学技术与工程, 2020, 20(3):1081-1085. TAO G H, ZHOU L. A novel emitter signal modulation classification method with deep learning[J]. Science Technology and Engineering, 2020, 20(3):1081-1085(in Chinese).
[39] PENG S L, JIANG H Y, WANG H X, et al. Modulation classification based on signal constellation diagrams and deep learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(3):718-727.
[40] WANG D S, ZHANG M, LI J, et al. Intelligent constellation diagram analyzer using convolutional neural network-based deep learning[J]. Optics Express, 2017, 25(15):17150-17166.
[41] DAI A, ZHANG H J, SUN H. Automatic modulation classification using stacked sparse auto-encoders[C]//2016 IEEE 13th International Conference on Signal Processing. Piscataway:IEEE Press, 2016:248-252.
[42] 郭蕴欣,张越,马宏.基于CNN的数字信号调制识别研究[J].电子测量技术, 2019, 42(14):112-116. GUO Y X, ZHANG Y, MA H. Research on modulation identification of digital signals based on convolutional neural network[J]. Electronic Measurement Technology, 2019, 42(14):112-116(in Chinese).
[43] SMITH A, EVANS M, DOWNEY J. Modulation classification of satellite communication signals using cumulants and neural networks[C]//2017 Cognitive Communications for Aerospace Applications Workshop (CCAA). Piscataway:IEEE Press, 2017:1-8.
[44] 白芃远,许华,孙莉.基于卷积神经网络与时频图纹理信息的信号调制方式分类方法[J].西北工业大学学报, 2019, 37(4):816-823. BAI P Y, XU H, SUN L. A recognition algorithm for modulation schemes by convolution neural network and spectrum texture[J]. Journal of Northwestern Polytechnical University, 2019, 37(4):816-823(in Chinese).
[45] 鲜佩,张晓芸,高昭昭.基于CNN的电磁辐射源目标识别算法[J].电子信息对抗技术, 2020, 35(2):34-38. XIAN P, ZHANG X Y, GAO Z Z. Electromagnetic radiation source target recognition algorithm based on CNN[J]. Electronic Information Warfare Technology, 2020, 35(2):34-38(in Chinese).
[46] 柏如龙,孙超,张世川. IFF信号单站无源定位技术[J].指挥信息系统与技术, 2017, 8(3):59-62. BAI R L, SUN C, ZHANG S C. Single observer passive location technology for IFF signals[J]. Command Information System and Technology, 2017, 8(3):59-62(in Chinese).
[47] 李明哲,李小将,李志亮.基于FDOA的无源定位算法研究现状与展望[J].兵工自动化, 2019, 38(4):28-34, 48. LI M Z, LI X J, LI Z L. Research status and prospect of passive localization methods based on frequency difference of arrival[J]. Ordnance Industry Automation, 2019, 38(4):28-34, 48(in Chinese).
[48] 吴魏.多站无源时频差高精度定位技术研究[D].郑州:解放军信息工程大学, 2015. WU W. Research on high accuracy passive localization using TDOA and FDOA with multiple receivers[D]. Zhengzhou:PLA Information Engineering University, 2015(in Chinese).
[49] XIONG W X, SCHINDELHAUER C, SO H C, et al. TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization[J]. Signal Processing, 2021, 178:107774.
[50] ENNASR O, TAN X B. Time-difference-of-arrival (TDOA)-based distributed target localization by A robotic network[J]. IEEE Transactions on Control of Network Systems, 2020, 7(3):1416-1427.
[51] OUYANG X X, HE Q, YANG Y X, et al. TDOA/FDOA estimation algorithm of frequency-hopping signals based on CAF coherent integration[J]. IET Communications, 2020, 14(2):331-336.
[52] CAO S, CHEN X, ZHANG X, et al. Combined weighted method for TDOA-based localization[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(5):1962-1971.
[53] XU C, WANG Z, WANG Y H, et al. Three passive TDOA-AOA receivers-based flying-UAV positioning in extreme environments[J]. IEEE Sensors Journal, 2020, 20(16):9589-9595.
[54] 杜宝舟,张冬晓,程二威.超宽带电磁脉冲对无人机辐照耦合仿真研究[J].计算机仿真, 2018, 35(4):29-32, 37. DU B Z, ZHANG D X, CHENG E W. Simulation study on irradiation coupling of UWB electromagnetic pulse to UAV[J]. Computer Simulation, 2018, 35(4):29-32, 37(in Chinese).
[55] 赵敏,许彤,程二威,等.无人机数据链电磁干扰机理和防护研究[J].强激光与粒子束, 2021, 33(3):49-56. ZHAO M, XU T, CHENG E W, et al. Mechanism and protection on the data link of UAV exposed to electromagnetic interference[J]. High Power Laser and Particle Beams, 2021, 33(3):49-56(in Chinese).
[56] 邵堃,雷迎科.对武器制导数据链灵巧干扰技术[J].弹箭与制导学报, 2020, 40(3):17-22. SHAO K, LEI Y K. Smart jamming technology for weapon guidance data link[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2020, 40(3):17-22(in Chinese).
[57] 段朋成,马文平.无人机上行链路抗欺骗干扰方案设计[J].测控技术, 2017, 36(10):147-150. DUAN P C, MA W P. Design of anti-spoofing scheme for UAV uplink[J]. Measurement&Control Technology, 2017, 36(10):147-150(in Chinese).
[58] 秦剑.基于生成对抗网络的信号重构[D].西安:西安电子科技大学, 2018. QIN J. Signal reconstruction based on Generative adversarial Networks[D]. Xi'an:Xidian University, 2018(in Chinese).
[59] 赵凡,金虎.基于GAN的通信干扰波形生成技术[J].系统工程与电子技术, 2021, 43(4):1080-1088. ZHAO F, JIN H. Communication jamming waveform generation technology based on GAN[J]. Systems Engineering and Electronics, 2021, 43(4):1080-1088(in Chinese).
[60] HUMPHREYS T E. Detection strategy for cryptographic GNSS anti-spoofing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2):1073-1090.
[61] RüGAMER A, KOWALEWSKI D. Jamming and spoofing of GNSS signals——an underestimated Risk?[C]//Wisdom of the Ages to the Challenges of the Modern World, 2015.
[62] FROST T, BUESNEL G. The vulnerability of GNSS timing receivers to spoofing attacks[C]//Workshop on Synchronization and Timing Systems (WSTS 2016), 2016.
[63] FROST T, BUESNEL G. Spoofing GNSS timing receivers[C]//2015 International Telecom Sync Forum, 2015.
[64] HE L, LI W, GUO C J, et al. Civilian unmanned aerial vehicle vulnerability to GPS spoofing attacks[C]//2014 Seventh International Symposium on Computational Intelligence and Design. Piscataway:IEEE Press, 2014:212-215.
[65] PSIAKI M L, HUMPHREYS T E, STAUFFER B. Attackers can spoof navigation signals without our knowledge. Here's how to fight back GPS lies[J]. IEEE Spectrum, 2016, 53(8):26-53.
[66] SEO S H, LEE B H, IM S H, et al. Effect of spoofing on unmanned aerial vehicle using counterfeited GPS signal[J]. Journal of Positioning, Navigation, and Timing, 2015, 4(2):57-65.
[67] KRASOVSKI S, PETOVELLO M, LACHAPELLE G. Ultra-tight GPS/INS receiver performance in the presence of jamming signals[C]//ION GNSS+2014, 2014
[68] LI C, WANG X. Jamming of unmanned aerial vehicle with GPS/INS integrated navigation system based on trajectory cheating[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2017, 49(3):420-427.
[69] CHEN J, LIU Y, LI Y H, et al. A method of traction partial jamming on GPS/INS integrated navigation[C]//Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Paris:Atlantis Press, 2013.
[70] 赫永磊,李冬,王肖飞.电子对抗手段干扰无人机GPS/INS导航系统效能分析[J].舰船电子工程, 2020, 40(12):44-47. HE Y L, LI D, WANG X F. Effectiveness analysis of electronic countermeasures jamming UAV GPS/INS navigation system[J]. Ship Electronic Engineering, 2020, 40(12):44-47(in Chinese).
[71] 毛虎,吴德伟,卢虎,等. GPSM码信号压制干扰样式效能分析[J].电子科技大学学报, 2015, 44(3):350-356. MAO H, WU D W, LU H, et al. Effectiveness analysis of the blanket jamming mode to GPS M code signal[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(3):350-356(in Chinese).
[72] 张欣然,李洪,杨春,等.欺骗干扰对GNSS矢量跟踪环路的影响[J].清华大学学报(自然科学版), 2022, 62(1):163-171. ZHANG X R, LI H, YANG C, et al. Influence of spoofing interference on GNSS vector tracking loops[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(1):163-171(in Chinese).
[73] 柳亚川,寇艳红.同步式GPS欺骗干扰信号生成技术研究与设计[J].北京航空航天大学学报, 2020, 46(4):814-821. LIU Y C, KOU Y H. Research and design of synchronous GPS spoofing signal generation technology[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(4):814-821(in Chinese).
[74] 陈军,王义冬,刘义,等.一种对GPS/INS组合导航的曳引式拉偏干扰[J].海军航空工程学院学报, 2012, 27(4):446-450. CHEN J, WANG Y D, LIU Y, et al. A method of traction partial jamming on GPS/INS integrated navigation[J]. Journal of Naval Aeronautical and Astronautical University, 2012, 27(4):446-450(in Chinese).
[75] 王文益,陈晨.配备GPS/INS组合导航系统的无人机诱捕方法[J].兵器装备工程学报, 2020, 41(11):212-217. WANG W Y, CHEN C. Method of trapping UAV equipped with GPS/INS integrated navigation system[J]. Journal of Ordnance Equipment Engineering, 2020, 41(11):212-217(in Chinese).
[76] LIU C B, WU C, HAN X E. Multiple-input multiple-output 3D imaging laser radar[C]//Proc SPIE 9674, AOPC 2015:Optical and Optoelectronic Sensing and Imaging Technology, 2015:714-718.
[77] MISU K, MIURA J. Specific person detection and tracking by a mobile robot using 3D LIDAR and ESPAR antenna[M]//Intelligent Autonomous Systems 13. Cham:Springer International Publishing, 2015:705-719.
[78] KOLB A, MEACLEM C, CHEN X Q, et al. Tree trunk detection system using LiDAR for a semi-autonomous tree felling robot[C]//2015 IEEE 10th Conference on Industrial Electronics and Applications. Piscataway:IEEE Press, 2015:84-89.
[79] 李慧,李岩,刘冰锋,等.激光干扰技术现状与发展及关键技术分析[J].激光与光电子学进展, 2011, 48(8):081407. LI H, LI Y, LIU B F, et al. Status, development and key technique analysis of laser jamming technology[J]. Laser&Optoelectronics Progress, 2011, 48(8):081407(in Chinese).
[80] 唐波,郭琨毅.合成孔径激光雷达电子对抗措施分析[J].红外与激光工程, 2007, 36(S2):472-475. TANG B, GUO K Y. The analysis of electronic countermeasures for SAL[J]. Infrared and Laser Engineering, 2007, 36(Sup 2):472-475(in Chinese).
[81] 张英远.激光对抗中的告警和欺骗干扰技术[D].西安:西安电子科技大学, 2012. ZHANG Y Y. Warning and deception jamming technology of laser countermeasure[D]. Xi'an:Xidian University, 2012(in Chinese).
[82] 王萃.激光雷达距离欺骗干扰技术研究[D].成都:电子科技大学, 2016. WANG C. Research on distance deception jamming technology of laser radar[D]. Chengdu:University of Electronic Science and Technology of China, 2016(in Chinese).
[83] 曹立华,陈长青.激光欺骗干扰技术与系统研究[J].光机电信息, 2011, 28(7):27-32. CAO L H, CHEN C Q. Technology and system of laser deception jamming[J]. OME Information, 2011, 28(7):27-32(in Chinese).
[84] STOTTELAAR B G B. Practical cyber-attacks on autonomous vehicles[D]. Enschede:University of Twente, 2015.
[85] SHIN H, KIM D, KWON Y, et al. Illusion and dazzle:Adversarial optical channel exploits against lidars for automotive applications[C]//Cryptographic Hardware and Embedded Systems-CHES 2017, 2017.
[86] CAO Y L, XIAO C W, CYR B, et al. Adversarial sensor attack on LiDAR-based perception in autonomous driving[C]//Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM, 2019:2267-2281.
[87] SUN J C, CAO Y L, CHEN Q A, et al. Towards robust LiDAR-based perception in autonomous driving:General black-box adversarial sensor attack and countermeasures[C]//SEC'20:Proceedings of the 29th USENIX Conference on Security Symposium, 2020:877-894.
[88] CAO Y L, XIAO C W, YANG D W, et al. Adversarial objects against LiDAR-based autonomous driving systems[DB/OL]. arXiv preprint:1907.05418, 2019.
[89] TU J, REN M Y, MANIVASAGAM S, et al. Physically realizable adversarial examples for LiDAR object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2020:13713-13722.
[90] 陈先中,刘荣杰,张森,等.煤矿地下毫米波雷达点云成像与环境地图导航研究进展[J].煤炭学报, 2020, 45(6):2182-2192. CHEN X Z, LIU R J, ZHANG S, et al. Development of millimeter wave radar imaging and SLAM in underground coal mine environment[J]. Journal of China Coal Society, 2020, 45(6):2182-2192(in Chinese).
[91] LI Y, LIU Y T, WANG Y P, et al. The millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU[J]. Sensors, 2020, 20(18):5421.
[92] MONTEATH G M. Environmental analysis of the sediments of southern Monterey Bay California[M]. Monterey:Naval Postgraduate School, 1965.
[93] ERALTAY K, ÖZKAZANÇ Y. Jamming of FMCW radars[C]//201220th Signal Processing and Communications Applications Conference (SIU). Piscataway:IEEE Press, 2012:1-4.
[94] ZAKERHAGHIGHI M R, MIVEHCHY M, SABAHI M F. Implementation and assessment of jamming effectiveness against an FMCW tracking radar based on a novel criterion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(6):4723-4733.
[95] 李阳,王楚媛,陈桥,等. FMCW车载雷达自适应干扰抑制方法[J].信号处理, 2021, 37(2):258-267. LI Y, WANG C Y, CHEN Q, et al. Mutual interference suppression method for FMCW automotive radar[J]. Journal of Signal Processing, 2021, 37(2):258-267(in Chinese).
[96] 许致火,施佺,孙玲.基于多径干扰认知的汽车毫米波雷达自适应波形优化方法[J].南京大学学报(自然科学), 2018, 54(3):597-603. XU Z H, SHI Q, SUN L. Adaptive millimeter-wave automotive radar waveform optimization based on learning of multi-path interference[J]. Journal of Nanjing University (Natural Science), 2018, 54(3):597-603(in Chinese).
[97] 卢雪怡,杨爱平,盛骥松.基于移频的距离波门拖引干扰方法分析与仿真[J].舰船电子对抗, 2018, 41(3):42-44, 95. LU X Y, YANG A P, SHENG J S. Analysis and simulation of range gate pull jamming method based on frequency shift[J]. Shipboard Electronic Countermeasure, 2018, 41(3):42-44, 95(in Chinese).
[98] 谌睿.间歇采样转发干扰特性研究与抗干扰波形设计[D].长沙:国防科技大学, 2017. CHEN R. Research on characteristics of interrupted sampling repeater jamming and anti-jamming waveform design[D]. Changsha:National University of Defense Technology, 2017(in Chinese).
[99] 钱升起.距离门拖引干扰与雷达抗干扰[D].北京:北京理工大学, 2016. QIAN S Q. Range gate pull off jamming and radar anti-jamming[D]. Beijing:Beijing Institute of Technology, 2016(in Chinese).
[100] ZHONG L J, QIN J Y, YANG X, et al. An accurate linear method for 3D line reconstruction for binocular or multiple view stereo vision[J]. Sensors, 2021, 21(2):658.
[101] 李文澜.基于立体视觉和机器学习的车辆检测方法的研究[D].长春:吉林大学, 2018. LI W L. Research on vehicle detection methods based on stereo vision and machine learning[D]. Changchun:Jilin University, 2018(in Chinese).
[102] MORENO-ESCOBAR J J, MORALES-MATAMOROS O, TEJEIDA-PADILLA R. Unmanned ground vehicle with stereoscopic vision for a safe autonomous exploration[J]. International Journal of Advanced Computer Science and Applications, 2019, 10(7):0100779.
[103] WEI X X, GUO Y, LI B. Black-box adversarial attacks by manipulating image attributes[J]. Information Sciences, 2021, 550:285-296.
[104] PARK S, SO J. On the effectiveness of adversarial training in defending against adversarial example attacks for image classification[J]. Applied Sciences, 2020, 10(22):8079.
[105] ZHANG H T, ZHOU W G, LI H Q. Contextual adversarial attacks for object detection[C]//2020 IEEE International Conference on Multimedia and Expo. Piscataway:IEEE Press, 2020:1-6.
[106] ZHANG H T, SHEN C. Best arm identification for both stochastic and adversarial multi-armed bandits[C]//2018 IEEE Information Theory Workshop. Piscataway:IEEE Press, 2018:1-5.
[107] 张高志,刘新平,邵明文.用于白盒目标攻击的GAN对抗样本生成[J].模式识别与人工智能, 2020, 33(9):830-838. ZHANG G Z, LIU X P, SHAO M W. Generating adversarial example with GAN for white-box target attacks[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(9):830-838(in Chinese).
[108] ZHAO G P, ZHANG M Y, LIU J J, et al. AP-GAN:Adversarial patch attack on content-based image retrieval systems[J]. GeoInformatica, 2020:1-31.
[109] KIM A, WAMPLER B, GOPPERT J, et al. Cyber attack vulnerabilities analysis for unmanned aerial vehicles[C]//Infotech@Aerospace 2012. Reston:AIAA, 2012.
[110] FOUDA R M. Security vulnerabilities of cyberphysical unmanned aircraft systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2018, 33(9):4-17.
[111] ZHI Y Y, FU Z J, SUN X M, et al. Security and privacy issues of UAV:A survey[J]. Mobile Networks and Applications, 2020, 25(1):95-101.
[112] VANITHA N, PADMAVATHI G. A study on various cyber-attacks and their classification in UAV assisted vehicular ad-hoc networks[M]//Communications in Computer and Information Science. Singapore:Springer, 2018:124-131.
[113] 任奎,孟泉润,闫守琨,等.人工智能模型数据泄露的攻击与防御研究综述[J].网络与信息安全学报, 2021, 7(1):1-10. REN K, MENG Q R, YAN S K, et al. Survey of artificial intelligence data security and privacy protection[J]. Chinese Journal of Network and Information Security, 2021, 7(1):1-10(in Chinese).
[114] 于赫.网联汽车信息安全问题及CAN总线异常检测技术研究[D].长春:吉林大学, 2016. YU H. Research on connected vehicle cyber security and anomaly detection technology for in-vehicle CAN bus[D]. Changchun:Jilin University, 2016(in Chinese).
[115] ZHOU H P, SHEN S G, LIU J H. Malware propagation model in wireless sensor networks under attack-defense confrontation[J]. Computer Communications, 2020, 162:51-58.
[116] DEEPA V, RADHA N. A survey on network intrusion system attacks classification using machine learning techniques[J]. IOP Conference Series:Materials Science and Engineering, 2021, 1022(1):012036.
[117] 石荣.历史上实际交战中雷达干扰效果评估方法回顾及启示[J].电子信息对抗技术, 2019, 34(5):49-56. SHI R. Retrospect and inspiration on radar jamming effectiveness evaluation method during actual electronic fighting in the history[J]. Electronic Information Warfare Technology, 2019, 34(5):49-56(in Chinese).
[118] OU J, ZHAO F, AI X F, et al. Quantitative evaluation for self-screening jamming effectiveness based on the changing characteristics of intercepted radar signals[C]//2016 CIE International Conference on Radar (RADAR). Piscataway:IEEE Press, 2016:1-5.
[119] QIN F T, MENG J, DU J, et al. Radar jamming effect evaluation based on AdaBoost combined classification model[C]//2013 IEEE 4th International Conference on Software Engineering and Service Science. Piscataway:IEEE Press, 2013:910-913.
[120] 刘松涛,雷震烁,葛杨,等.电子对抗干扰效果评估技术综述[J].中国电子科学研究院学报, 2020, 15(4):306-317, 342. LIU S T, LEI Z S, GE Y, et al. A review on evaluation technology of jamming effects of electronic countermeasure[J]. Journal of China Academy of Electronics and Information Technology, 2020, 15(4):306-317, 342(in Chinese).
[121] 耿凯迪.雷达干扰效果在线评估技术研究[D].西安:西安电子科技大学, 2019. GENG K D. Research on the online effectiveness evaluation of radar jamming[D]. Xi'an:Xidian University, 2019(in Chinese).
[122] 赵耀东,徐旺.一种基于雷达状态变化的干扰效果在线评估方法[J].电子信息对抗技术, 2016, 31(3):42-46. ZHAO Y D, XU W. A method of real-time electronic attack effectiveness evaluation based on state transition of radar[J]. Electronic Information Warfare Technology, 2016, 31(3):42-46(in Chinese).
[123] 邢强,贾鑫,朱卫纲,等.基于干扰方的雷达在线干扰效果评估[J].电子信息对抗技术, 2018, 33(6):57-62. XING Q, JIA X, ZHU W G, et al. Radar online jamming effect evaluation based on jamming side[J]. Electronic Information Warfare Technology, 2018, 33(6):57-62(in Chinese).
[124] 王伟,杨俊安,刘辉,等.基于干扰效果在线评估的行为参数分析[J].电子信息对抗技术, 2016, 31(6):76-80. WANG W, YANG J N, LIU H, et al. Behavioral parametric analysis based on evaluation of jamming effect online[J]. Electronic Information Warfare Technology, 2016, 31(6):76-80(in Chinese).
[125] 崔双洋.认知电子对抗中的目标状态识别技术研究[D].北京:北京理工大学, 2016. CUI S Y. Research of the target state recognition techniques in cognitive electronic countermeasure[D]. Beijing:Beijing Institute of Technology, 2016(in Chinese).

Countering unmanned ground system:A review of key technologies

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