Optimal Jamming Frequency Selection for Cognitive Jammer based on Reinforcement Learning

被引：0
作者：
Wang, Lulu [1 ]
Peng, Jinlin [1 ]
Xie, Zhidong [1 ]
Zhang, Yi [2 ]
机构：
[1] Natl Innovat Inst Def Technol, Artificial Intelligence Res Ctr, Beijing, Peoples R China
[2] State Key Lab Complex Electromagnet Environm Effe, Luoyang, Peoples R China
来源：
2019 2ND IEEE INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP) | 2019年
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
jamming frequency selection; reinforcement learning; Q-learning; MIMO RADAR;
D O I：
10.1109/icicsp48821.2019.8958575
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a cognitive jammer is developed which can adaptively and optimally jam the radar and protect the target from being detected. The interaction of the cognitive jammer and the environment is modeled as a finite Markov Decision Process based on the framework of reinforcement learning. Q-learning algorithm is used to solve the optimal jamming frequency selection problem. After several interactions, the jammer can learn the radar's strategy and optimize its jamming frequency to achieve a larger jamming-plus-noise to signal ratio (JNSR) during the whole process. Numerical results are given to illustrate the effectiveness of the proposed method. Compared with a random jamming frequency selection method, the JNSR of the proposed method is significantly larger.
引用
收藏
页码：39 / 43
页数：5
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