A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning

TL;DR

This paper introduces FARDA, a deep reinforcement learning-based framework that significantly accelerates the deployment of cognitive radar systems for anti-jamming purposes, achieving near-evolutionary coverage speeds.

Contribution

The paper presents a novel neural network inference framework for radar deployment, drastically reducing deployment time compared to traditional evolutionary algorithms.

Findings

Deployment speed increased by approximately 7,000 times.

Coverage comparable to evolutionary algorithms.

Ablation experiments validate each component's importance.

Abstract

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.