基于隐式对手建模的策略重构抗智能干扰方法

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中图分类号：TN795 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.04.32

Abstract：With the continuous advancement of artificial intellgence technology，intellgent jamming seriously threatens wireless signal transmission，and traditional anti-jamming algorithms are insuficient. Based on the above isue，using reinforcement learning algorithms as foundation，implicit opponent modeling techniques areintroduced，encoding the jamming agent’s strategy implicitly in the neural network input and determining communication frequencies through neural network decisions.In response to thenon-stationary nature of intelligent jammingstrategies，profit trendsare monitored to identify whether jamming strategies are switching and strategy reconstruction technology is proposed.Multi-scale windowdetection isutilized to identifythe start pointof experiential failureand discard failed experiences.Learning rate is simultaneously reset to accelerate the convergence speed of the neural network.Experimental results demonstrate that during the relative convergence phase，the proposed method's transmission success rate is increased by over 25% compared to the deep Q-network anti-jamming method.

Keywords：anti-jamming；intelligent jamming；deep reinforcement learning；trend detection；implicit opponent modeling

0引言

在人工智能技术的推动下，干扰机逐渐演变为具备高度智能化和自适应能力的干扰智能体，对通信系统的稳定性和安全性造成极大的危害[1-3]。（剩余17705字）