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

2025-12-05

AI Summary

Cognitive radar deployment for electronic warfare faces challenges of excessive time consumption and susceptibility to local optima during anti-jamming adaptation. Method: This paper proposes FARDA, an end-to-end deep reinforcement learning framework featuring a novel heatmap perception neural module for real-time modeling of interference environments, coupled with a customized reward function jointly optimizing coverage quality and deployment efficiency to mitigate local optima. Contribution/Results: Experiments demonstrate that FARDA achieves coverage performance comparable to conventional evolutionary algorithms while accelerating deployment by approximately 7,000×. Ablation studies confirm the critical roles of both the heatmap perception module and the redesigned reward mechanism. This work establishes an efficient, scalable paradigm for rapid, adaptive cognitive radar deployment in dynamic electromagnetic environments.

Technology Category

• Intelligent Robots: Cognitive Robotics
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Develops a fast anti-jamming radar deployment algorithm using reinforcement learning
• Addresses slow and suboptimal evolutionary methods for radar placement
• Achieves comparable coverage with significantly faster deployment speeds

Innovation

• Uses deep reinforcement learning for radar deployment
• Integrates neural modules to perceive heatmap information
• Introduces a new reward format for faster optimization