用强化学习加速反干扰雷达部署，速度提升7000倍。
A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
- 用深度强化学习建模雷达部署为端到端任务
- 覆盖效果相当，部署速度提升约7000倍
- 适合需要快速响应的战场雷达系统设计
现代战争中，快速部署认知雷达以应对干扰仍是关键挑战，更高效的部署可实现更快的目标探测。现有方法多基于进化算法，耗时长且易陷入局部最优。本文通过神经网络高效推理，提出全新框架FARDA（Fast Anti-Jamming Radar Deployment Algorithm）。将雷达部署问题建模为端到端任务，设计深度强化学习算法，引入集成神经模块感知热图信息，并构建新型奖励机制。实验表明，本方法在覆盖效果接近进化算法的同时，雷达部署速度提升约7000倍。消融实验验证了FARDA各组件的必要性。
原文摘要 · Abstract (English)
The fast deployment of cognitive radar to counter jamming remains a critical challenge in modern warfare, where more efficient deployment leads to quicker detection of targets. Existing methods are primarily based on evolutionary algorithms, which are time-consuming and prone to falling into local optima. We tackle these drawbacks via the efficient inference of neural networks and propose a brand new framework: Fast Anti-Jamming Radar Deployment Algorithm (FARDA). We first model the radar deployment problem as an end-to-end task and design deep reinforcement learning algorithms to solve it, where we develop integrated neural modules to perceive heatmap information and a brand new reward format. Empirical results demonstrate that our method achieves coverage comparable to evolutionary algorithms while deploying radars approximately 7,000 times faster. Further ablation experiments confirm the necessity of each component of FARDA.
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