A Genetic Algorithm Approach to Anti-Jamming UAV Swarm Behavior

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.