基于优化SAC算法的智能干扰功率分配决策

Intelligent Jamming Power Allocation Decision Based on Optimized SAC Algorithm

摘要:

针对传统认知电子战中干扰资源调度不合理的问题，构建了一个由干扰机协同掩护目标飞机的突防场景，提出了一种基于优化软演员-评论家（soft Actor-Critic, SAC）算法的智能干扰功率分配决策方法，并将其与主流的近端策略优化（proximal policy optimization，PPO）算法进行了对比，最后对优化SAC算法中的关键优化机制进行深入分析，探讨了影响算法性能的主要因素。试验结果表明，所提方法在收敛速度和稳定性方面均优于PPO算法，尤其在干扰资源受限的复杂环境中，能够显著提升干扰资源的利用率，从而验证了该方法的可行性与有效性。

Abstract:

To address the problem of unreasonable jamming resource scheduling in traditional cognitive electronic warfare, a penetration confrontation scenario was constructed where jamming aircraft cooperatively shield a target aircraft. An intelligent jamming power allocation decision method based on an optimized soft Actor-Critic (SAC) algorithm was proposed and then compared with the mainstream proximal policy optimization (PPO) algorithm. Additionally, key optimization mechanisms within the optimized SAC algorithm were analyzed in depth, and the primary factors influencing its performance were explored. Experimental results demonstrate that the proposed method outperforms PPO in terms of both convergence speed and stability. In particular, under complex conditions with limited jamming resources, it significantly improves jamming resource utilization, thus validating the feasibility and effectiveness of the proposed method.