The Allocation of Jamming Resources Based on Double Q-learning Algorithm

Authors

Xingyuan Huang, College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
Yanyi Li, College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;

Abstract

Abstract: In modern warfare, the multifunctional trend of radars, even multiple radars detecting targets together, enhances the anti-jamming capability of radars. However, the traditional jamming system still follows a fixed jamming strategy, and the real-time performance of decision-making facing large numbers of radars is poor. And the cognitive jamming study is urgent. The concept of reinforcement learning is explained and the difference between Q learning algorithm and double Q learning algorithm is compared. The reinforcement learning algorithm is used to establish a model based on cognitive electronic warfare to realize the allocation of radar jamming strategies. The simulation of the decision-making method shows that the two reinforcement learning algorithms can accomplish the task of jamming strategy allocation, and the double-Q learning algorithm works better in a multi-radar environment. It shows that the reinforcement learning algorithm can perform autonomous learning and complete the cognitive decision-making for the allocation of interference resources.

Recommended Citation

Huang, Xingyuan and Li, Yanyi (2021) "The Allocation of Jamming Resources Based on Double Q-learning Algorithm," Journal of System Simulation: Vol. 33: Iss. 8, Article 6.
DOI: 10.16182/j.issn1004731x.joss.20-0253
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss8/6

First Page

1801

Revised Date

2020-08-04

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0253

Last Page

1808

CLC

TN974;TP391

Recommended Citation

Huang Xingyuan, Li Yanyi. The Allocation of Jamming Resources Based on Double Q-learning Algorithm[J]. Journal of System Simulation, 2021, 33(8): 1801-1808.

DOI

10.16182/j.issn1004731x.joss.20-0253