A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
This addresses a critical challenge in modern warfare for radar deployment, but it is incremental as it improves speed over prior methods.
The paper tackled the problem of fast deployment of cognitive radar to counter jamming by proposing a new reinforcement learning-based framework, achieving coverage comparable to existing evolutionary algorithms while being approximately 7,000 times faster.
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.