通信干扰信道和功率智能决策算法  

Intelligent Decision-making for Selection of Communication Jamming Channel and Power

作　　者：周成[1] 林茜[1] 马丛珊 应涛[1] 满欣[1] ZHOU Cheng;LIN Qian;MA Congshan;YING Tao;MAN Xin(College of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China)

机构地区：[1]海军工程大学电子工程学院,武汉430033

出　　处：《电子与信息学报》2024年第10期3957-3965,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61501484)。

摘　　要：智能干扰是一种利用环境反馈自主学习干扰策略,对敌方通信链路进行有效干扰的技术。然而,现有的智能干扰研究大多假设干扰机能够直接获取通信质量反馈(如误码率或丢包率),这在实际对抗环境中难以实现,限制了智能干扰的应用范围。为了解决这一问题,该文将通信干扰问题建模为马尔科夫决策过程(MDP),综合考虑干扰基本原则和通信目标行为变化制定干扰效能衡量指标,提出了一种改进的策略爬山算法(IPHC)。该算法按照“观察(Observe)-调整(Orient)-决策(Decide)-行动(Act)”的OODA闭环,实时观察通信目标变化,灵活调整干扰策略,运用混合策略决策,实施通信干扰。仿真结果表明,在通信目标采用确定性规避策略时,所提算法能够较快收敛到最优干扰策略,并且其收敛耗时较Q-learning算法至少缩短2/3;当通信目标变换策略时,能够自适应学习,重新调整到最优干扰策略。在通信目标采用混合性规避策略时,所提算法也能够快速收敛,取得较优的干扰效果。Intelligent jamming is a technique that utilizes environmental feedback information and autonomous learning of jamming strategies to effectively disrupt the communication links of the enemy.However,most existing research on intelligent jamming assumes that jammers can directly access the feedback of communication quality indicators,such as bit error rate or packet loss rate.This assumption is difficult to achieve in practical adversarial environments,thus limiting the applicability of intelligent jamming.To address this issue,the communication jamming problem is modeled as a Markov Decision Process(MDP),and by considering both the fundamental principles of jamming and the dynamic behavior of communication objectives,an Improved Policy Hill-Climbing(IPHC)algorithm is proposed.This algorithm follows an OODA loop of“Observe-Orient-Decide-Act”,continuously observes the changes of communication objectives in real time,flexibly adjusts jamming strategies,and applies a mixed strategy decision-making to execute communication jamming.Simulation results demonstrate that when the communication objectives adopt deterministic evasion strategies,the proposed algorithm can quickly converge to the optimal jamming strategy,and the convergence time is at least two-thirds shorter than that of the Q-learning algorithm.When the communication objectives switch evasion strategies,the algorithm can adaptively learn and readjust to the optimal jamming strategy.In the case of communication objectives using mixed evasion strategies,the proposed algorithm also achieves fast convergence and obtains superior jamming effects.

关 键 词：智能干扰 干扰效能评估 混合性策略 改进策略爬山算法 

分 类 号：TN975[电子电信—信号与信息处理]