战术跳频系统智能抗干扰决策

作为一种有效的抗干扰方法，跳频（Frequency hopping, FH）技术已被广泛应用于战术通信系统来提高在强对抗环境下军事通信网络的可靠性。跳频通信网络面临的主要威胁是具有灵敏的频谱侦察和频率捷变能力的跟踪干扰机。为提高战术跳频通信系统在跟踪干扰攻击环境中的抗脆性和吞吐量，本文提出了一种基于双深度Q网络（Double deep Q-network, DDQN）的功率和跳速联合抗干扰决策方法。该算法将战术电台发射机与跟踪干扰机之间的对抗建模为马尔可夫决策过程（Markov decision process, MDP），其中干扰器通过调整频谱扫描速率提高干扰效能，战术电台终端则将接收状态反馈信息作为算法输入，根据决策网络的输出调整数据传输的发射功率和跳频速率。该算法模型在未知环境状态和干扰参数的情况下，通过与环境的交互学习更新网络参数，逐渐收敛于最佳功率和跳速联合控制策略，以使跳频通信系统的平均吞吐量最大化。仿真结果表明，相比传统的无模型抗干扰方法，本文所提算法在跟踪干扰环境下能够更有效改善跳频系统的抗干扰性能。

Intelligent Anti-Jamming Strategy for Tactical Frequency-Hopping System

? ?As an effective anti-jamming approach， frequency-hopping （FH） technology has been widely applied to tactical communication system to improve communication network's throughput under strong confrontation environment. Key challenges for tactical wireless communication network face are the follower jammer with responsive spectrum reconnaissance and frequency agility. In response to improving the anti-fragile performance and throughput of tactical FH communication system， this paper investigates a double deep Q-network （DDQN） based anti-jamming scheme. The interactions between a radio transmitter and a follower jammer are formulated as a Markov decision process （MDP）， in which the jammer adjusts to improve the interference performance， and the radio terminal feeds the state feedback information into algorithm input parameters， then selects the transmit power and hopping rate according to the output of policy network. With the aim of maximizing the system throughput the proposed approach updates network parameters through interacting with environment simultaneously without knowing the environment state and jamming model， and converges to an optimal solution with joint power and hopping rate control. Simulations results demonstrate that the proposed scheme can provide better effective interference resistance in follower jamming environment， comparing with other model free learning methods in contrast.