复杂电磁环境下基于HRDQN的智能干扰决策算法OA

To address the issues of slow convergence speed and poor energy efficiency of existing intelligent jamming decision-making in communication countermeasures scenarios,a hierarchical Rainbow deep Q-network(HRDQN)algo-rithm was proposed.Firstly,a communication system model subject to non-cooperative intelligent jamming was formu-lated,and the jamming decision-making process was modeled as a Markov decision process(MDP),deriving the sup-pression coefficient threshold to quantify jamming effectiveness.Secondly,the action space and decision-making method of the agent were designed to improve decision-making efficiency based on a hierarchical structure.Finally,the reward function was designed to combine the suppression coefficient threshold with the estimated jamming-to-signal ratio(JSR)to guarantee stable convergence of the algorithm.Simulation results demonstrate that the proposed algorithm rapidly gen-erates ideal jamming decisions while reducing power consumption,and outperforms traditional algorithms in conver-gence speed,thereby corroborating the merits of the proposed algorithm.