基于贝叶斯Q学习的无人机集群抗干扰智能快跳频算法

Intelligent Fast Frequency Hopping Algorithm for UAV Swarm Anti-Jamming Based on Bayesian Q-Learning

针对无人机集群抗干扰通信问题,提出了一种以同时实现信息传输速率最大化和跳频开销最小化为目标的智能快跳频算法。首先在传统Q学习基础上,利用近期信息价值增益选择跳频点,再依据对环境的观测信息,并运用矩更新方法修正基于高斯-伽马分布模型的Q值,进而实现了对无人机集群快跳频策略性能的提升。仿真结果表明,相较于随机快跳频和基于传统Q学习的智能快跳频算法,所提算法能更快地学习到性能更佳的快跳频策略。

In order to cope with the problem of anti-jamming communication for UAV swarm,an intelligent fast frequency hopping algorithm is proposed with the goal of maximizing the information transmission rate and minimizing the frequency hopping overhead simultaneously.Firstly,based on the traditional Q-learning,a myopic value of perfect information is used to select the transmission channel,and then according to the observation information of the environment,the moment update method is adopted to correct the Q value based on the Gauss-Gama distribution model,which thus improves the performance of the fast frequency hopping strategy for UAV swarm.The simulation results show that compared with the random fast frequency hopping algorithm and the Q-learning based intelligent fast frequency hopping algorithm,the proposed scheme can learn fast frequency hopping strategy with faster convergence.