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.

翻译：在现代战争中，快速部署认知雷达以对抗干扰仍是一个关键挑战，更高效的部署能更快地探测目标。现有方法主要基于进化算法，这些方法耗时且易陷入局部最优。我们通过神经网络的高效推理解决了这些缺陷，并提出了一种全新的框架：快速抗干扰雷达部署算法（FARDA）。我们首先将雷达部署问题建模为端到端任务，并设计深度强化学习算法来解决它，其中我们开发了集成神经模块以感知热图信息，并设计了一种全新的奖励格式。实验结果表明，我们的方法在达到与进化算法相当的覆盖范围的同时，部署雷达的速度提高了约7000倍。进一步的消融实验证实了FARDA各组成部分的必要性。