GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar

Author:

Xia Liqun1ORCID,Wang Lulu23,Xie Zhidong23,Gao Xin4

Affiliation:

1. National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100010, China

2. Intelligent Game and Decision Laboratory, Academy of Military Science, Beijing 100091, China

3. Chinese People’s Liberation Army 32806 Unit, Academy of Military Science, Beijing 100091, China

4. The 85th Detachment, Chinese People’s Liberation Army 95969 Unit, Wuhan 430000, China

Abstract

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Reference28 articles.

1. Haigh, K., and Andrusenko, J. (2021). Cognitive Electronic Warfare: An Artificial Intelligence Approach, Artech House.

2. Ruixue, Z., Guifen, X., Yue, Z., and Hengze, L. (2015, January 16–18). Coherent signal processing method for frequency-agile radar. Proceedings of the 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Qingdao, China.

3. Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.

4. Joint channel and power optimisation for multi-user anti-jamming communications: A dual mode Q-learning approach;Zhang;IET Commun.,2022

5. Jamming resilient tracking using POMDP-based detection of hidden targets;Jiang;IEEE Trans. Inf. Forensics Secur.,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3