机载雷达告警接收机发展现状及趋势

王星; 王俊迪; 金政芝; 周一鹏; 陈游

doi:10.12000/JR22200

机载雷达告警接收机发展现状及趋势

DOI: 10.12000/JR22200

1.
空军工程大学航空工程学院西安 710038
2.
93128部队北京 100843
3.
空军航空大学长春 130022

基金项目: 国家自然科学基金(62001489)，陕西省自然科学基金(2021JM-225)

详细信息

作者简介:
王　星，教授，博士生导师，主要研究方向为电子对抗原理与技术

王俊迪，博士生，研究方向为电子对抗原理与技术

金政芝，硕士，工程师，主要研究方向为电子对抗装备总体

周一鹏，博士，讲师，主要研究方向为电子对抗原理与技术

陈　游，博士，副教授，主要研究方向为电子对抗原理与技术

通讯作者:
王俊迪 qxwangjundi@sina.com

责任主编：董春曦 Corresponding Editor: DONG Chunxi
中图分类号: TN95
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- 被引次数: 0
出版历程
- 收稿日期: 2022-09-30
- 修回日期: 2022-12-02
- 网络出版日期: 2022-12-11
- 刊出日期: 2023-04-28

Current Situation and Development Demands for a Radar Warning Receiver System

1.
Aviation Engineering School, Air Force Engineering University, Xi’an 710038, China
2.
Unit 93128, Beijing 100843, China
3.
Aviation University Air Force Aviation University Air Force, Changchun 130022, China

Funds: The National Natural Science Foundation of China (62001489), Shaanxi Natural Science Foundation (2021JM-225)

More Information

Corresponding author: WANG Jundi, qxwangjundi@sina.com

摘要

摘要: 随着信息技术的发展和空战模式的改变，机载雷达告警接收机(RWR)成为现代战机不可缺少的电子战系统。为了更好地理解机载RWR，该文从接收机体制角度考虑，将机载RWR的系统架构划分成两个阶段，对每个阶段的特点和组成进行了分析。接着详细阐述了机载RWR的信号处理流程，并且梳理了与信号分选、信号识别和威胁评估相关的技术。最后，从实际运用出发，系统总结了机载RWR在复杂电磁环境中和应对新体制雷达中面临的挑战以及未来的发展需求。
- 机载雷达告警接收机 /
- 系统架构 /
- 信号处理流程 /
- 分选识别 /
- 威胁评估 /
- 发展需求
Abstract: With the development in information technology and the change of air combat mode, Radar Warning Receiver (RWR) have become indispensable electronic warfare equipment for modern fighters. To better understand the airborne RWR system, this study divides the airborne RWR architecture into two stages from the perspective of receiver system. The characteristics and components of the architecture are analyzed. Then, this study elaborates on the signal processing flow of airborne RWR, and classifies the technologies and algorithms related to signal sorting, signal identification and threat assessment. Finally, this study systematically summarizes the challenges and future demand analysis of airborne RWR in complex battlefield environments and in dealing with new radar systems.
- Radar Warming Receiver (RWR) /
- System architecture /
- Signal processing flow /
- Sorting and identification /
- Threat assessment /
- Development demands

HTML全文

图 1 模拟体制机载RWR基本架构图

Figure 1. Basic architecture of analog airborne RWR

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图 2 数据总线机载RWR系统架构

Figure 2. Architecture of airborne RWR system based on data bus

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图 3 机载RWR信号处理流程

Figure 3. Airborne RWR signal processing flow

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图 4 脉冲稀释处理流程

Figure 4. Pulse dilution processing flow

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图 5 目标识别处理流程

Figure 5. Target recognition processing flow

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参考文献(78)

[1]	李峥. 机载雷达告警技术发展趋势[J]. 电子信息对抗技术, 2008, 23(3): 51–54. doi: 10.3969/j.issn.1674-2230.2008.03.013 LI Zheng. The development trend of the airborne radar warning receiver[J]. Electronic Information Countermeasure Technology, 2008, 23(3): 51–54. doi: 10.3969/j.issn.1674-2230.2008.03.013
[2]	周帆, 陈兴凯, 韩壮志, 等. 机载雷达告警接收机的现状及技术发展趋势[J]. 飞航导弹, 2014(2): 41–46. doi: 10.16338/j.issn.1009-1319.2014.02.010 ZHOU Fan, CHEN Xingkai, HAN Zhuangzhi, et al. The present situation and technical development trend of airborne radar warning receiver[J]. Aerodynamic Missile Journal, 2014(2): 41–46. doi: 10.16338/j.issn.1009-1319.2014.02.010
[3]	GRANT P M and COLLINS. Introduction to electronic warfare[J]. Communications Radar & Signal Processing Iee Proceedings F, 1982.
[4]	张璟珲, 黄新松, 吴志建. 机载自卫电子对抗装备作战目标分析[J]. 舰船电子对抗, 2012, 35(6): 10–13. doi: 10.16426/j.cnki.jcdzdk.2012.06.012 ZHANG Jinghui, HUANG Xinsong, and WU Zhijian. Campaign targets analysis of airborne self-defense electronic countermeasure equipment[J]. Shipboard Electronic Countermeasure, 2012, 35(6): 10–13. doi: 10.16426/j.cnki.jcdzdk.2012.06.012
[5]	高松, 滕克难, 段哲. 美军核心电子战支援装备及其发展趋势分析[J]. 飞航导弹, 2019(11): 12–17. doi: 10.16338/j.issn.1009-1319.20190114 GAO Song, TENG Kenan, and DUAN Zhe. Analysis on the core EW support equipment and its development trend of the US army[J]. Aerodynamic Missile Journal, 2019(11): 12–17. doi: 10.16338/j.issn.1009-1319.20190114
[6]	刘都群, 郭冠宇. 俄罗斯电子战领域发展现状与趋势分析[J]. 飞航导弹, 2019(10): 16–19. doi: 10.16338/j.issn.1009-1319.20190843 LIU Duqun and GUO Guanyu. Analysis on the development status and trend of Russian electronic warfare[J]. Aeronautical Missile, 2019(10): 16–19. doi: 10.16338/j.issn.1009-1319.20190843
[7]	ADAMY D L, 王燕, 朱松, 译. EW101: 电子战基础[M]. 北京: 电子工业出版社, 2009: 42–65. ADAMY D L, WANG Yan, and ZHU Song. translation. EW101: A First Course in Electronic Warfare[M]. Beijing: Publishing House of Electronics Industry, 2009: 42–65.
[8]	MAINI A K. Battlefield lasers and opto-electronics systems[J]. Defence Science Journal, 2010, 60(2): 189–196. doi: 10.14429/dsj.60.339
[9]	OSTROWSKI R, CYWIŃSKI A, and STRZELEC M. Electronic warfare in the optical band: Main features, examples and selected measurement data[J]. Defence Technology, 2021, 17(5): 1636–1649. doi: 10.1016/j.dt.2020.09.007
[10]	鲁旭. 机载雷达告警接收机的发展现状与发展前景展望[J]. 信息通信, 2020(5): 64–65. LU Xu. Development status and prospect of airborne radar warning receiver[J]. Information &Communications, 2020(5): 64–65.
[11]	邵超. 雷达发射机和接收机设计[D]. [硕士论文], 西安电子科技大学, 2012. SHAO Chao. Design of radar transmitter and radar receiver[D]. [Master dissertation], Xidian University, 2012.
[12]	ADAMY D L, 朱松, 王燕, 译. EW102: 电子战进阶[M]. 北京: 电子工业出版社, 2009: 6–29. ADAM D L, ZHU Song, and WANG Yan. translation. EW102: A Second Course in Electronic Warfare[M]. Beijing: Publishing House of Electronics Industry, 2009: 6–29.
[13]	赵国庆. 雷达对抗原理[M]. 2版. 西安: 西安电子科技大学出版社, 2012: 95–100. ZHAO Guoqing. Principle of Radar Countermeasure[M]. 2nd ed. Xi’an: Xidian University Press, 2012: 95–100.
[14]	陈杨, 陈新年, 颜振亚, 等. 一种综合雷达信号侦察预处理方法[J]. 现代雷达, 2016, 38(4): 78–81. doi: 10.16592/j.cnki.1004-7859.2016.04.018 CHEN Yang, CHEN Xinnian, YAN Zhenya, et al. A method of integrated radar signal reconnaissance pretreatment[J]. Modern Radar, 2016, 38(4): 78–81. doi: 10.16592/j.cnki.1004-7859.2016.04.018
[15]	张永顺, 童宁宁, 赵国庆. 雷达电子战原理[M]. 北京: 国防工业出版社, 2006: 73–78. ZHANG Yongshun, TONG Ningning, and ZHAO Guoqing. Principles of Radar Electronic Warfare[M]. Beijing: National Defense Industry Press, 2006: 73–78.
[16]	MARDIA H K. New techniques for the deinterleaving of repetitive sequences[J]. IEE Proceedings F (Radar and Signal Processing), 1989, 136(4): 149–154. doi: 10.1049/ip-f-2.1989.0025
[17]	MILOJEVIĆ D J and POPOVIĆ B M. Improved algorithm for the deinterleaving of radar pulses[J]. IEE Proceedings F (Radar and Signal Processing), 1992, 139(1): 98–104. doi: 10.1049/ip-f-2.1992.0012
[18]	NISHIGUCHI K and KOBAYASHI M. Improved algorithm for estimating pulse repetition intervals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 407–421. doi: 10.1109/7.845217
[19]	樊甫华, 史英春, 秦立龙. 一种脉冲重复间隔固定序列快速检测方法[J]. 电子与信息学报, in press, 2022. doi: 10.11999/JEIT220127. FAN Fuhua, SHI Yingchun, and QIN Lilong. A method for fast detecting sequence with stable pulse repetition interval[J]. Journal of Electronics & Information, in press, 2022. doi: 10.11999/JEIT220127.
[20]	CHANDRA V and BAJPAI R C. ESM data processing parametric deinterleaving approach[C]. TENCON’92 - Technology Enabling Tomorrow, Melbourne, Australia, 1992.
[21]	翁永祥, 赵万磊, 郑志娟. 基于PCA联合K-Means聚类的雷达信号分选算法[J]. 舰船电子对抗, 2020, 43(2): 38–42, 60. doi: 10.16426/j.cnki.jcdzdk.2020.02.009 WENG Yongxiang, ZHAO Wanlei, and ZHENG Zhijuan. Radar signal sorting algorithm based on PCA combined K-Means clustering[J]. Shipboard Electronic Countermeasure, 2020, 43(2): 38–42, 60. doi: 10.16426/j.cnki.jcdzdk.2020.02.009
[22]	何佩佩. 被动探测信号聚类分选算法研究[D]. [硕士论文], 中国舰船研究院, 2016. HE Peipei. Research of passive detection signal sorting based on clustering algorithm[D]. [Master dissertation], China Ship Research and Development Academy, 2016.
[23]	董柏娴, 汤建龙. 基于网格密度聚类的雷达信号在线分选算法[J]. 航天电子对抗, 2014, 30(6): 45–48. doi: 10.3969/j.issn.1673-2421.2014.06.013 DONG Baixian and TANG Jianlong. A radar signal online sorting algorithm based on grid-density clustering[J]. Aerospace Electronic Warfare, 2014, 30(6): 45–48. doi: 10.3969/j.issn.1673-2421.2014.06.013
[24]	刘鲁涛, 王璐璐, 李品, 等. DSets-DBSCAN无参数聚类的雷达信号分选算法[J]. 国防科技大学学报, 2022, 44(4): 158–163. doi: 10.11887/j.cn.202204017 LIU Lutao, WANG Lulu, LI Pin, et al. Radar signal sorting algorithm for DSets-DBSCAN without parameter clustering[J]. Journal of National University of Defense Technology, 2022, 44(4): 158–163. doi: 10.11887/j.cn.202204017
[25]	王星, 陈相, 周一鹏, 等. 一种基于改进DBSCAN的雷达信号分选算法[J]. 空军工程大学学报(自然科学版), 2021, 22(3): 47–54. doi: 10.3969/j.issn.1009-3516.2021.03.008 WANG Xing, CHEN Xiang, ZHOU Yipeng, et al. A radar signal sorting algorithm based on improved DBSCAN algorithm[J]. Journal of Air Force Engineering University (Natural Science Edition), 2021, 22(3): 47–54. doi: 10.3969/j.issn.1009-3516.2021.03.008
[26]	李强. 雷达辐射源信号分选技术研究[D]. [硕士论文], 西安电子科技大学, 2019. LI Qiang. Research on radar emitter signal sorting technology[D]. [Master dissertation], Xidian University, 2019.
[27]	王嘉慰. 多参数聚类雷达信号分选技术研究[D]. [硕士论文], 哈尔滨工程大学, 2019. WANG Jiawei. Research on radar signal sorting technology of multi-parameter clustering[D]. [Master dissertation], Harbin Engineering University, 2019.
[28]	IATSENKO D, MCCLINTOCK P V E, and STEFANOVSKA A. Extraction of instantaneous frequencies from ridges in time-frequency representations of signals[J]. Signal Processing, 2016, 125: 290–303. doi: 10.1016/j.sigpro.2016.01.024
[29]	BUFLER T D and NARAYANAN R M. Radar classification of indoor targets using support vector machines[J]. IET Radar, Sonar & Navigation, 2016, 10(8): 1468–1476. doi: 10.1049/iet-rsn.2015.0580
[30]	CAO Ru, CAO Jiuwen, MEI Jianping, et al. Radar emitter identification with bispectrum and hierarchical extreme learning machine[J]. Multimedia Tools and Applications, 2019, 78(20): 28953–28970. doi: 10.1007/s11042-018-6134-y
[31]	王星, 符颖, 陈游, 等. 基于多重分形和半监督EM的LPI雷达信号识别[J]. 控制与决策, 2018, 33(11): 1941–1949. doi: 10.13195/j.kzyjc.2017.0799 WANG Xing, FU Ying, CHEN You, et al. Radar signal recognition based on multi-fractal and semi-supervised EM algorithm[J]. Control and Decision, 2018, 33(11): 1941–1949. doi: 10.13195/j.kzyjc.2017.0799
[32]	王星, 呙鹏程, 田元荣, 等. 基于BDS-GD的低截获概率雷达信号识别[J]. 北京航空航天大学学报, 2018, 44(3): 583–592. doi: 10.13700/j.bh.1001-5965.2017.0146 WANG Xing, GUO Pengcheng, TIAN Yuanrong, et al. LPI radar signal recognition based on BDS-GD[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 583–592. doi: 10.13700/j.bh.1001-5965.2017.0146
[33]	DUDCZYK J and KAWALEC A. Identification of emitter sources in the aspect of their fractal features[J]. Bulletin of the Polish Academy of Sciences:Technical Sciences, 2013, 61(3): 623–628. doi: 10.2478/bpasts-2013-0065
[34]	李东海. 基于专家系统的雷达识别方法[J]. 舰船电子对抗, 2014, 37(5): 10–13, 58. doi: 10.16426/j.cnki.jcdzdk.2014.05.012 LI Donghai. Radar identification method based on expert system[J]. Shipboard Electronic Countermeasure, 2014, 37(5): 10–13, 58. doi: 10.16426/j.cnki.jcdzdk.2014.05.012
[35]	BRONSTEIN M M, BRUNA J, LECUN Y, et al. Geometric deep learning: Going beyond Euclidean data[J]. IEEE Signal Processing Magazine, 2017, 34(4): 18–42. doi: 10.1109/MSP.2017.2693418
[36]	LIN C M, CHEN Y M, and HSUEH C S. A self-organizing interval type-2 fuzzy neural network for radar emitter identification[J]. International Journal of Fuzzy Systems, 2014, 16(1): 20–30.
[37]	ZHU Mingzhe, FENG Zhenpeng, and ZHOU Xianda. A novel data-driven specific emitter identification feature based on machine cognition[J]. Electronics, 2020, 9(8): 1308. doi: 10.3390/electronics9081308
[38]	XIAO Zhiling and YAN Zhenya. Radar emitter identification based on novel time-frequency spectrum and convolutional neural network[J]. IEEE Communications Letters, 2021, 25(8): 2634–2638. doi: 10.1109/LCOMM.2021.3084043
[39]	陈森森. 基于RNN的雷达辐射源分类识别算法研究[D]. [硕士论文], 西安电子科技大学, 2019. CHEN Sensen. Research on classification and recognition algorithm of radar signal based on RNN[D]. [Master dissertation], Xidian University, 2019.
[40]	HUANG Kejun, YANG Junan, LIU Hui, et al. Deep adversarial neural network for specific emitter identification under varying frequency[J]. Bulletin of the Polish Academy of Sciences-Technical Sciences, 2021, 69(2): e136737. doi: 10.24425/bpasts.2021.136737
[41]	高翔. 基于深度置信网络的雷达辐射源识别[D]. [硕士论文], 西安电子科技大学, 2018. GAO Xiang. Radar emitter recognition based on depth confidence network[D]. [Master dissertation], Xidian University, 2018.
[42]	叶文强, 俞志富, 王虎帮, 等. 基于卷积神经网络辐射源信号识别算法[J]. 计算机仿真, 2019, 36(9): 33–37. doi: 10.3969/j.issn.1006-9348.2019.09.009 YE Wenqiang, YU Zhifu, WANG Hubang, et al. Recognition algorithm of emitter signals based on CNN[J]. Computer Simulation, 2019, 36(9): 33–37. doi: 10.3969/j.issn.1006-9348.2019.09.009
[43]	王亮. 基于神经网络的雷达信号识别研究[D]. [硕士论文], 哈尔滨工程大学, 2021. WANG Liang. Research on radar signal recognition based on neural network[D]. [Master dissertation], Harbin Engineering University, 2021.
[44]	SUN Xia, XU Jingting, JIANG Changmeng, et al. Extreme learning machine for multi-label classification[J]. Entropy, 2016, 18(6): 225. doi: 10.3390/e18060225
[45]	CHEN Wenbin, FU Kun, ZUO Jiawei, et al. Radar emitter classification for large data set based on weighted-xgboost[J]. IET Radar, Sonar & Navigation, 2017, 11(8): 1203–1207. doi: 10.1049/iet-rsn.2016.0632
[46]	唐晓婧, 陈维高, 席龙飞, 等. 基于AdaBoost和决策树的雷达辐射源识别方法[J]. 电子信息对抗技术, 2018, 33(4): 16–20, 58. doi: 10.3969/j.issn.1674-2230.2018.04.004 TANG Xiaojing, CHEN Weigao, XI Longfei, et al. The radar emitter identification algorithm based on Ada boost and decision tree[J]. Electronic Information Warfare Technology, 2018, 33(4): 16–20, 58. doi: 10.3969/j.issn.1674-2230.2018.04.004
[47]	刘艺林, 李胜勇, 李伟鹏, 等. 基于随机森林的雷达辐射源识别算法研究[J]. 电光与控制, 2022, 29(2): 108–112. doi: 10.3969/j.issn.1671-637X.2022.02.023 LIU Yilin, LI Shengyong, LI Weipeng, et al. A radar emitter identification algorithm based on random forest[J]. Electronics Optics &Control, 2022, 29(2): 108–112. doi: 10.3969/j.issn.1671-637X.2022.02.023
[48]	苏畅. 基于强化学习的雷达辐射源识别技术研究与应用[D]. [硕士论文], 北京邮电大学, 2021. SU Chang. Research and application of radar emitter recognition technology based on reinforcement learning[D]. [Master dissertation], Beijing University of Posts and Telecommunications, 2021.
[49]	冷鹏飞, 徐朝阳. 一种深度强化学习的雷达辐射源个体识别方法[J]. 兵工学报, 2018, 39(12): 2420–2426. doi: 10.3969/j.issn.1000-1093.2018.12.016 LENG Pengfei and XU Chaoyang. Specific emitter identification based on deep reinforcement learning[J]. Acta Armamentarii, 2018, 39(12): 2420–2426. doi: 10.3969/j.issn.1000-1093.2018.12.016
[50]	VISNEVSKI N, HAYKIN S, KRISHNAMURTHY V, et al. Hidden Markov models for radar pulse train analysis in electronic warfare[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, USA, 2005.
[51]	VISNEVSKI N, KRISHNAMURTHY V, WANG A, et al. Syntactic modeling and signal processing of multifunction radars: A stochastic context-free grammar approach[J]. Proceedings of the IEEE, 2007, 95(5): 1000–1025. doi: 10.1109/JPROC.2007.893252
[52]	欧健. 多功能雷达行为辨识与预测技术研究[D]. [博士论文], 国防科学技术大学, 2017. OU Jian. Research on behavior recognition and prediction techniques against multi-function radar[D]. [Ph. D. dissertation], National University of Defense Technology, 2017.
[53]	马爽, 柳征, 姜文利. 生物基因技术在多功能雷达信号解析中的应用[J]. 电子学报, 2013, 41(12): 2374–2381. doi: 10.3969/j.issn.0372-2112.2013.12.009 MA Shuang, LIU Zheng, and JIANG Wenli. The application of gene techniques to multifunction radar signal analysis[J]. Acta Electronica Sinica, 2013, 41(12): 2374–2381. doi: 10.3969/j.issn.0372-2112.2013.12.009
[54]	ZHANG Kun, KONG Weiren, LIU Peipei, et al. Assessment and sequencing of air target threat based on intuitionistic fuzzy entropy and dynamic VIKOR[J]. Journal of Systems Engineering and Electronics, 2018, 29(2): 305–310. doi: 10.21629/JSEE.2018.02.11
[55]	ZHAO Ruojing, YANG Fengbao, JI Linna, et al. Dynamic air target threat assessment based on interval-valued intuitionistic fuzzy sets, game theory, and evidential reasoning methodology[J]. Mathematical Problems in Engineering, 2021, 2021: 6652706. doi: 10.1155/2021/6652706
[56]	HUANG Lida, CAI G, YUAN Hongyong, et al. A hybrid approach for identifying the structure of a Bayesian network model[J]. Expert Systems with Applications, 2019, 131: 308–320. doi: 10.1016/j.eswa.2019.04.060
[57]	GAO Yang, LI Dongsheng, and ZHONG Hua. A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment[J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103276. doi: 10.1016/j.engappai.2019.103276
[58]	翟翔宇. 基于全连接神经网络的空战目标威胁评估方法研究[D]. [硕士论文], 中北大学, 2020. ZHAI Xiangyu. Research on threat assessment method of air combat target based on fully connected neural network[D]. [Master dissertation], North University of China, 2020.
[59]	王永坤, 郑世友, 邓晓波. 基于极限学习机的目标智能威胁感知技术[J]. 雷达科学与技术, 2020, 18(4): 387–393. doi: 10.3969/k.issn.1672-2337.2020.04.006 WANG Yongkun, ZHENG Shiyou, and DENG Xiaobo. Intelligent threat perception of aerial target based on extreme learning machine[J]. Radar Science and Technology, 2020, 18(4): 387–393. doi: 10.3969/k.issn.1672-2337.2020.04.006
[60]	MA Shidong, ZHANG Hongzhi, and YANG Guoqing. Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation[J]. Aerospace Science and Technology, 2017, 67: 49–53. doi: 10.1016/j.ast.2017.03.033
[61]	张才坤, 朱战霞, 冯琦, 等. 基于区间数雷达图的可视化空战威胁评估[J]. 系统工程与电子技术, 2016, 38(5): 1052–1058. doi: 10.3969/j.issn.1001-506X.2016.05.13 ZHANG Caikun, ZHU Zhanxia, FENG Qi, et al. Visualization threat assessment for air combat based on interval-radar chart[J]. Systems Engineering and Electronics, 2016, 38(5): 1052–1058. doi: 10.3969/j.issn.1001-506X.2016.05.13
[62]	奚之飞, 徐安, 寇英信, 等. 基于灰主成分的空战目标威胁评估[J]. 系统工程与电子技术, 2021, 43(1): 147–155. doi: 10.3969/j.issn.1001-506X.2021.01.18 XI Zhifei, XU An, KOU Yingxin, et al. Air combat target threat assessment based on gray principal component[J]. Systems Engineering and Electronics, 2021, 43(1): 147–155. doi: 10.3969/j.issn.1001-506X.2021.01.18
[63]	胡涛, 王栋, 孙曜, 等. 基于改进CRITIC-LRA和灰色逼近理想解排序法的空战威胁评估[J]. 兵工学报, 2020, 41(12): 2561–2569. doi: 10.3969/j.issn.1000-1093.2020.12.022 HU Tao, WANG Dong, SUN Yao, et al. Air combat threat assessment of improved CRITIC-LRA and grey TOPSIS[J]. Acta Armamentarii, 2020, 41(12): 2561–2569. doi: 10.3969/j.issn.1000-1093.2020.12.022
[64]	杨爱武, 李战武, 李宝, 等. 基于动态变权重的空战态势评估[J]. 兵工学报, 2021, 42(7): 1553–1563. doi: 10.3969/j.issn.1000-1093.2021.07.023 YANG Aiwu, LI Zhanwu, LI Bao, et al. Air combat situation assessment based on dynamic variable weight[J]. Acta Armamentarii, 2021, 42(7): 1553–1563. doi: 10.3969/j.issn.1000-1093.2021.07.023
[65]	杨爱武, 李战武, 徐安, 等. 基于RS-CRITIC的空战目标威胁评估[J]. 北京航空航天大学学报, 2020, 46(12): 2357–2365. doi: 10.13700/j.bh.1001-5965.2019.0638 YANG Aiwu, LI Zhanwu, XU An, et al. Threat assessment of air combat target based on RS-CRITIC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2357–2365. doi: 10.13700/j.bh.1001-5965.2019.0638
[66]	范翔宇, 王红卫, 索中英, 等. 基于粗糙集-信息熵的辐射源威胁评估方法[J]. 北京航空航天大学学报, 2016, 42(8): 1755–1761. doi: 10.13700/j.bh.1001-5965.2015.0663 FAN Xiangyu, WANG Hongwei, SUO Zhongying, et al. Radiator threat evaluating method based on rough set and information entropy[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(8): 1755–1761. doi: 10.13700/j.bh.1001-5965.2015.0663
[67]	呙鹏程, 王星, 周东青, 等. 基于熵权的灰色关联辐射源威胁评估[J]. 空军工程大学学报(自然科学版), 2016, 17(6): 58–63. doi: 10.3969/j.issn.1009-3516.2016.06.011 GUO Pengcheng, WANG Xing, ZHOU Dongqing, et al. A grey correlation method based on entropy weight in emitter threat assessment[J]. Journal of Air Force University of Engineering (Natural Science Edition), 2016, 17(6): 58–63. doi: 10.3969/j.issn.1009-3516.2016.06.011
[68]	董鹏宇, 王红卫, 陈游. 基于博弈论的GRA-TOPSIS辐射源威胁评估方法[J]. 北京航空航天大学学报, 2020, 46(10): 1973–1981. doi: 10.13700/j.bh.1001-5965.2019.0543 DONG Pengyu, WANG Hongwei, and CHEN You. GRA-TOPSIS emitter threat assessment method based on game theory[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1973–1981. doi: 10.13700/j.bh.1001-5965.2019.0543
[69]	徐宇恒, 程嗣怡, 庞梦洋. 基于CRITIC-TOPSIS的动态辐射源威胁评估[J]. 北京航空航天大学学报, 2020, 46(11): 2168–2175. doi: 10.13700/j.bh.1001-5965.2019.0585 XU Yuheng, CHENG Siyi, and PANG Mengyang. Dynamic radiator threat assessment based on CRITIC-TOPSIS[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(11): 2168–2175. doi: 10.13700/j.bh.1001-5965.2019.0585
[70]	张莹, 王红卫, 陈游. 基于ICW-RCM的辐射源组合威胁评估[J]. 系统工程与电子技术, 2018, 40(3): 557–562. doi: 10.3969/j.issn.1001-506X.2018.03.11 ZHANG Ying, WANG Hongwei, and CHEN You. Combined emitter threat assessment based on ICWM-RCM[J]. Systems Engineering and Electronics, 2018, 40(3): 557–562. doi: 10.3969/j.issn.1001-506X.2018.03.11
[71]	李健伟, 刘璘, 吴宏超, 等. 机载有源相控阵雷达给告警器带来的威胁[J]. 雷达与对抗, 2014, 34(2): 14–17, 34. doi: 10.19341/j.cnki.issn.1009-0401.2014.02.005 LI Jianwei, LIU Lin, WU Hongchao, et al. Threats to radar warning receiver that airborne active phased array radars bring[J]. Radar &ECM, 2014, 34(2): 14–17, 34. doi: 10.19341/j.cnki.issn.1009-0401.2014.02.005
[72]	GURBUZ S Z, GRIFFITHS H D, CHARLISH A, et al. An overview of cognitive radar: Past, present, and future[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34(12): 6–18. doi: 10.1109/MAES.2019.2953762
[73]	HAYKIN S. Cognitive radar: A way of the future[J]. IEEE Signal Processing Magazine, 2006, 23(1): 30–40. doi: 10.1109/MSP.2006.1593335
[74]	蒋平虎, 苏萍贞. 关于机载电子战技术发展的思考[J]. 航天电子对抗, 2018, 34(2): 56–60. doi: 10.16328/j.htdz8511.2018.02.015 JIANG Pinghu and SU Pingzhen. On the consideration of the development of airborne electronic warfare technology[J]. Aerospace Electronic Warfare, 2018, 34(2): 56–60. doi: 10.16328/j.htdz8511.2018.02.015
[75]	谷康, 李亚明, 刘平. 智能化电子对抗装备发展[J]. 国防科技, 2018, 39(2): 78–81, 90. doi: 10.13943/j.issn1671-4547.2018.02.13 GU Kang, LI Yaming, and LIU Ping. Research on the development of intelligent electronic countermeasures equipment[J]. National Defense Science and Technology, 2018, 39(2): 78–81, 90. doi: 10.13943/j.issn1671-4547.2018.02.13
[76]	王星, 周一鹏, 陈游, 等. 一种采用前后端并行处理机制的机载雷达告警器及其数据处理方法[P]. 中国, 112130141A, 2020. WANG Xing, ZHOU Yipeng, CHEN You, et al. An airborne radar warning device with front and rear end parallel processing mechanism and its data processing method[P]. CN, 112130141A, 2020.
[77]	SUTTERFIELD B, HOSCHETTE J A, and ANTON P. Future integrated modular avionics for jet fighter mission computers[C]. 2008 IEEE/AIAA 27th Digital Avionics Systems Conference, St. Paul, USA, 2008.
[78]	SHAJI R S, DEV V S, and BRINDHA T. A methodological review on attack and defense strategies in cyber warfare[J]. Wireless Networks, 2019, 25(6): 3323–3334. doi: 10.1007/s11276-018-1724-1