Anti-Jamming Path Planning Using GCN for Multi-UAV

TL;DR

This paper introduces a GCN-based path planning method for UAV swarms to predict and evade jamming areas, enhancing robustness and efficiency in hostile environments.

Contribution

It presents a novel GCN-based approach for UAV swarm path planning that predicts jamming locations without prior knowledge, improving anti-jamming capabilities.

Findings

Accurate prediction of jamming areas demonstrated in simulations.

Successful UAV swarm evasion and mission completion.

Method shows robustness and scalability in various scenarios.

Abstract

This paper addresses the increasing significance of UAVs (Unmanned Aerial Vehicles) and the emergence of UAV swarms for collaborative operations in various domains. However, the effectiveness of UAV swarms can be severely compromised by jamming technology, necessitating robust antijamming strategies. While existing methods such as frequency hopping and physical path planning have been explored, there remains a gap in research on path planning for UAV swarms when the jammer's location is unknown. To address this, a novel approach, where UAV swarms leverage collective intelligence to predict jamming areas, evade them, and efficiently reach target destinations, is proposed. This approach utilizes Graph Convolutional Networks (GCN) to predict the location and intensity of jamming areas based on information gathered from each UAV. A multi-agent control algorithm is then employed to disperse the UAV swarm, avoid jamming, and regroup upon reaching the target. Through simulations, the effectiveness of the proposed method is demonstrated, showcasing accurate prediction of jamming areas and successful evasion through obstacle avoidance algorithms, ultimately achieving the mission objective. Proposed method offers robustness, scalability, and computational efficiency, making it applicable across various scenarios where UAV swarms operate in potentially hostile environments.