A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
By: Wencheng Cai , Xuchao Gao , Congying Han and more
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.
Similar Papers
A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
Cryptography and Security
Stops phone network jammers automatically.
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Machine Learning (CS)
Learns to send messages even when jammed.
Deep Reinforcement Learning for Real-Time Drone Routing in Post-Disaster Road Assessment Without Domain Knowledge
Machine Learning (CS)
Helps drones find safe roads after disasters.