A Genetic Algorithm Approach to Anti-Jamming UAV Swarm Behavior
Tiago Silva, António Grilo
TL;DR
The paper tackles anti-jamming for UAV swarms by jointly optimizing formation, directional antennas, and traffic routing to protect the main coordination channel. It introduces a encapsulated genetic algorithm with an outer layer for UAV positions and an inner layer for beam directions, guided by a non-linear objective that favors higher end-to-end capacity and resilience to interference. Simulation results across static, fixed-area, and mobile-swarm scenarios show substantial performance gains over omnidirectional baselines, while highlighting computational costs and centralized control as notable constraints. The work lays a foundation for future decentralization and learning-based improvements, such as deep reinforcement learning and multi-agent approaches, to reduce reliance on jam-resilient signaling channels.
Abstract
In recent years, Unmanned Aerial Vehicles (UAVs) have brought a new true revolution to military tactics. While UAVs already constitute an advantage when operating alone, multi-UAV swarms expand the available possibilities, allowing the UAVs to collaborate and support each other as a team to carry out a given task. This entails the capability to exchange information related with situation awareness and action coordination by means of a suitable wireless communication technology. In such scenario, the adversary is expected to disrupt communications by jamming the communication channel. The latter becomes the Achilles heel of the swarm. While anti-jamming techniques constitute a well covered topic in the literature, the use of intelligent swarm behaviors to leverage those techniques is still an open research issue. This paper explores the use of Genetic Algorithms (GAs) to jointly optimize UAV swarm formation, beam-steering antennas and traffic routing in order to mitigate the effect of jamming in the main coordination channel, under the assumption that a more robust and low data rate channel is used for formation management signaling. Simulation results show the effectiveness of proposed approach. However, the significant computational cost paves the way for further research.