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Multi-agent autonomous electronic jamming system based on information sharing |
Pan ZHANG1,2( ),Hua DING3,Ying-er ZHANG4,Bing-ning LI5,Jiang-tao HUANG-FU4,Zhong-he JIN1,2,*( ) |
1. Micro-Satellite Research Center, Zhejiang University, Hangzhou 310027, China 2. Zhejiang Key Laboratory of Micro-nano Satellite Research, Hangzhou 310027, China 3. Aerospace Equipment Department, PLA Strategic Support Force, Beijing 100094, China 4. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China 5. Xi'an Satellite Control Center, Hangzhou 310000, China |
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Abstract Multi-agent electronic jammers and information sharing mechanism was used to break through the limited information perception ability of the original single sensor in order to realize the effective jamming of radiation source in complex electromagnetic environment. Then real-time dynamic situation information sharing was realized, and the intelligent decision cognition ability of electronic jammer was enhanced. The rapid convergence of the microsecond-level about the jamming parameters was achieved by the multi-agent independent electronic jamming system based on information sharing, and the information error probability in a single-jammer conditions was reduced. The experimental results show that the jamming parameter update strategy based on the method is adjusted in real time with the dynamic change of situational environment. A more autonomous and intelligent cognitive electronic jamming system is realized.
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Received: 15 March 2021
Published: 05 January 2022
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Fund: 国家杰出青年科学基金资助项目(61525403);省级重点研发计划资助项目(209C05004);之江国际青年人才基金资助项目 |
Corresponding Authors:
Zhong-he JIN
E-mail: zhangpan_zju@zju.edu.cn;jinzh@zju.edu.cn
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基于信息共享的多智能体自主电子干扰系统
为了实现电子干扰机在复杂电磁环境中对辐射源的有效干扰,利用多智能体电子干扰机与信息共享机制,突破原有单传感器的有限信息感知能力,实现实时动态态势信息共享,增强电子干扰机的智能决策认知能力. 基于信息共享的多智能体自主电子干扰系统实现了μs级的干扰参数快速收敛,减小了单传感器的信息误差概率. 通过仿真实验表明,基于该方法的干扰参数更新策略随着态势环境的动态变化而实时自主调整,实现了更加自主与智能的认知电子干扰系统.
关键词:
信息共享,
多智能体电子干扰机,
Q-Learning,
态势感知
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[1] |
李振初 人工智能技术在电子战中的应用[J]. 电子对抗技术, 1988, (2): 31- 43 LI Zhen-chu Application of AI technology in EW[J]. Electronic Warfare Technology, 1988, (2): 31- 43
|
|
|
[2] |
MENG Xiang-ping, GAO Yan. Electric systems analysis [M]. Beijing: Higher Education Press, 2004: 3-21.
|
|
|
[3] |
ZHENG X L, LIU J H, WANG B S Analysis of range_doppler coherent jamming performance against radar with the RATR technique[J]. Electronic Information Warfare Technology, 2017, 32 (5): 52- 56
|
|
|
[4] |
ZHAO Y L, WANG X S, WANG G Y Tracking technique for radar network in the presence of multi-range-false-target deception jamming[J]. Acta Electronic Sinica, 2007, 3 (3): 454- 458
|
|
|
[5] |
LOPATKA J, KULPA K, SZCZEPANKIEWICZ M, et al. Cognitive systems in electronic warfare[C]// XI Conference on Reconnaissance and Electronic Warfare Systems. Oltarzew: [s.n.], 2017: 1041802.
|
|
|
[6] |
KINGSLEY N, GUERCI J R. Adaptive amplifier module technique to support cognitive RF architectures[C]// Radar Conference.[S.l.]: IEEE, 2014.
|
|
|
[7] |
DARPA. Behavior learning for adaptive electronic warfare [EB/OL]. (2010-10-06). https://www.fbo.gov.
|
|
|
[8] |
DARPA. Adaptive radar countermeasures [EB/OL]. (2012-08-27). https://www.fbo.gov.
|
|
|
[9] |
王沙飞, 鲍雁飞, 李岩 认知电子战体系结构与技术[J]. 中国科学: 信息科学, 2018, 48 (12): 1603- 1613 WANG Sha-fei, BAO Yan-fei, LI Yan The architecture and technology of cognitive electronic warfare[J]. Scientia Sinica: Informationis, 2018, 48 (12): 1603- 1613
doi: 10.1360/N112018-00153
|
|
|
[10] |
邢强, 贾鑫, 朱卫纲 基于Q-学习的智能雷达对抗[J]. 系统工程与电子技术, 2018, 40 (5): 1031- 1035 XING Qiang, JIA Xin, ZHU Wei-gang Intelligent radar countermeasure based on Q-learning[J]. Systems Engineering and Electronics, 2018, 40 (5): 1031- 1035
doi: 10.3969/j.issn.1001-506X.2018.05.11
|
|
|
[11] |
李云杰, 朱云鹏, 高梅国 基于Q-学习算法的认知雷达对抗过程设计[J]. 北京理工大学学报, 2015, 35 (11): 1194- 1199 LI Yun-jie, ZHU Yun-peng, GAO Mei-guo Design of cognitive radar jamming based on Q-learning algorithm[J]. Transactions of Beijing Institute of Technology, 2015, 35 (11): 1194- 1199
|
|
|
[12] |
HAO T, CUI C, GONG Y Efficient low-PAR waveform design method for extended target estimation based on information theory in cognitive radar[J]. Entropy, 2019, 21 (3): 261
doi: 10.3390/e21030261
|
|
|
[13] |
SU H, CHEN M Z Q Multi-agent containment control with input saturation on switching topologies[J]. IET Control Theory and Applications, 2015, 9 (3): 399- 409
doi: 10.1049/iet-cta.2014.0393
|
|
|
[14] |
FU J, WAN Y, WEN G, et al Distributed robust global containment control of second-order multiagent systems with input saturation[J]. IEEE Transactions on Control of Network Systems, 2019, 6 (4): 1- 10
doi: 10.1109/TCNS.2019.2956892
|
|
|
[15] |
YAN Y, HUANG J Cooperative output regulation of discrete-time linear time-delay multi-agent systems under switching network[J]. Neurocomputing, 2017, 241 (7): 108- 114
|
|
|
[16] |
YAN Y, HUANG J Cooperative output regulation of discrete-time linear time-delay multi-agent systems[J]. IET Control Theory Appliance, 2016, 10 (16): 2019- 2026
doi: 10.1049/iet-cta.2016.0106
|
|
|
[17] |
LI Z, REN W, LIU X, et al Distributed containment control of multi-agent systems with general linear dynamics in the presence of multiple leaders[J]. International Journal of Robust and Nonlinear Control, 2013, 23 (5): 534- 547
doi: 10.1002/rnc.1847
|
|
|
[18] |
BUI V H, NGUYEN T T, KIM H M. Distributed operation of wind farm for maximizing output power: a multi-agent deep reinforcement learning approach [J]. Access IEEE, 2020, 8: 173136-173146.
|
|
|
[19] |
何俊辉. 侦察干扰一体化处理器FPGA软件设计[D]. 哈尔滨: 哈尔滨工程大学, 2017. HE Jun-hui. FPGA software design for reconnaissance and jamming integration processor [D]. Harbin: Harbin Engineering University, 2017.
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