保障无人机安全通信的自主飞行3D路径规划  

Autonomous Flight 3D Path Planning for Secure UAV Communication

作　　者：孙卉 赵睿[1] 游亚璇 沙德双 SUN Hui;ZHAO Rui;YOU Yaxuan;SHA Deshuang(Xiamen Mobile Multimedia Communication Lab,Huaqiao University,Xiamen,Fujian 361021,China)

机构地区：[1]华侨大学厦门市移动多媒体通信实验室,福建厦门361021

出　　处：《信号处理》2022年第5期1027-1036,共10页Journal of Signal Processing

基　　金：福建省自然科学基金(2019J01055)。

摘　　要：在无人机服务多个地面移动用户并存在一个窃听者窃听信息的安全通信场景中,为了最大化安全速率,本文提出一种新的深度强化学习算法对无人机3D轨迹进行优化,该算法名为正确轨迹深度确定性策略梯度算法(correct trajectory-deep deterministic policy gradient,CT-DDPG)。CT-DDPG算法使用多个深度神经网络与环境交互,采用修正输出层激活函数值的方式,代替传统的使用多个激活函数的方法,简化深度神经网络结构。同时对无人机的飞行轨迹进行修正,使无人机始终处于安全速率最大化的最佳位置。与其他强化学习算法相比,该算法训练时间短,执行时能实时更新无人机的位置。仿真结果表明,所提出的算法能够快速收敛,在保障无人机安全通信的情况下完成飞行任务。In the secure communication scenario where the unmanned aerial vehicle(UAV)served multiple ground mobile users and there was an eavesdropper eavesdropping information,in order to maximize the secrecy rate,this paper proposed a new deep reinforcement learning algorithm to optimize the 3D trajectory of the UAV. This algorithm was named correct trajectory-deep deterministic policy gradient(CT-DDPG). CT-DDPG algorithm used multiple deep neural networks to interact with the environment,and modified the activation function value of the output layer to replace the traditional method of using multiple activation functions to simplify the structure of the deep neural network. At the same time,the flight trajectory of the UAV was modified so that the UAV was always in the best position to maximize the secrecy rate.Compared with other reinforcement learning algorithms,this algorithm had short training time and can update the position of UAV in real time. The simulation results show that the proposed algorithm can converge quickly and complete the flight mission while ensuring the secure communication of UAV.

关 键 词：安全速率 深度强化学习 无人机3D轨迹 深度确定性策略梯度 

分 类 号：TN918[电子电信—通信与信息系统]