一种基于深度强化学习的协同通信干扰决策算法
A Collaborative Communication Jamming Decision Algorithm Based on Deep Reinforcement Learning
查看参考文献13篇
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文摘
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针对协同电子战中跳频通信干扰协同决策难题,通过构建“整体优化、逐站决策”的协同决策模型,基于深度强化学习技术,设计了在Actor-Critic算法架构下融合优势函数的决策算法,并在奖励函数中嵌入专家激励机制以提高算法的探索能力,采用集中式训练方法优化决策网络,使算法能够输出资源利用率最高的干扰方案,并大幅提高决策效率.仿真结果表明,相比于现有智能决策算法,本文算法给出的干扰方案能够节约8%干扰资源,决策效率提高50%以上,具有较大实用价值. |
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其他语种文摘
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In order to solve the problem of collaborative decision-making of frequency-hopping communication jamming in collaborative electronic warfare, based on deep reinforcement learning, a collaborative jamming decision-making algorithm based on actor-critic algorithm framework is proposed, which fuses dominant functions by building a collaborative decision-making model of "overall optimization and making decision station by station". An expert experience mechanism is embedded in the reward function to improve the exploration ability of the algorithm, and the decision network is optimized by the distributed execution-centralized training method, so that the algorithm can output the jamming scheme with the highest resource utilization rate and greatly improve the efficiency of decision-making. The simulation results show that, compared with the existing intelligent decision algorithms, the jamming scheme presented in this paper can save 8% of the interference resources and improve the decision efficiency by more than 50%, which is of great practical value. |
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来源
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电子学报
,2022,50(6):1301-1309 【核心库】
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DOI
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10.12263/DZXB.20210814
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关键词
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深度强化学习
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通信干扰决策
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干扰资源分配
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优势函数
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专家激励
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地址
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空军工程大学信息与导航学院, 陕西, 西安, 710077
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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电子技术、通信技术 |
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基金
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国家自然科学基金青年基金
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文献收藏号
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CSCD:7240119
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参考文献 共
13
共1页
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1.
Xiao L. User-centric view of jamming games in cognitive radio networks.
IEEE Transactions on Information Forensics and Security,2015,10(12):2578-2590
|
CSCD被引
5
次
|
|
|
|
|
2.
Amuru S. On jamming against wireless networks.
IEEE Transactions on Wireless Communications,2017,16(1):412-428
|
CSCD被引
3
次
|
|
|
|
|
3.
Zhou P. Near-optimal and practical jamming-resistant energy-efficient cognitive radio communications.
IEEE Transactions on Information Forensics and Security,2017,12(11):2807-2822
|
CSCD被引
2
次
|
|
|
|
|
4.
Jiang H Q. Optimal allocation of cooperative jamming resource based on hybrid quantumbehaved particle swarm optimization and genetic algorithm.
IET Radar, Sonar & Navigation,2017,11(1):185-192
|
CSCD被引
8
次
|
|
|
|
|
5.
施伟. 基于深度强化学习的多机协同空战方法研究.
自动化学报,2021,47(7):1610-1623
|
CSCD被引
40
次
|
|
|
|
|
6.
Zhuansun S S. An algorithm for jamming strategy using OMP and MAB.
EURASIP Journal on Wireless Communications and Networking,2019,2019(1):1-11
|
CSCD被引
1
次
|
|
|
|
|
7.
Amuru S. Jamming bandits-A novel learning method for optimal jamming.
IEEE Transactions on Wireless Communications,2016,15(4):2792-2808
|
CSCD被引
14
次
|
|
|
|
|
8.
颛孙少帅. 采用双层强化学习的干扰决策算法.
西安交通大学学报,2018,52(2):63-69
|
CSCD被引
6
次
|
|
|
|
|
9.
许华. 一种通信对抗干扰资源分配智能决策算法.
电子与信息学报,2021,43(11):3086-3095
|
CSCD被引
5
次
|
|
|
|
|
10.
Mnih V. Asynchronous methods for deep reinforcement learning.
Proceedings of the 33rd International Conference on Machine Learning (ICML),2016:1928-1937
|
CSCD被引
5
次
|
|
|
|
|
11.
Mnih V. Human-level control through deep reinforcement learning.
Nature,2015,518(7540):529-533
|
CSCD被引
1121
次
|
|
|
|
|
12.
Van Huynh N. "Jam me if You can: " defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications.
IEEE Journal on Selected Areas in Communications,2019,37(11):2603-2620
|
CSCD被引
4
次
|
|
|
|
|
13.
陈思光. 基于深度强化学习的云边协同计算迁移研究.
电子学报,2021,49(1):157-166
|
CSCD被引
7
次
|
|
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|
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