Dynamic UAV Swarm Confrontation: An Imitation Based on Mobile Adaptive Networks

Cooperative unmanned aerial vehicle (UAV) swarms could expand the mission capability of the single UAV and the overall combat effectiveness. We consider the dynamic scenarios where an UAV swarm confronts multiple maneuvering high-value targets (HVTs) and multiple maneuvering attackers. In such hostile scenarios, each UAV of the UAV swarm would generally strive to survive through collaboratively offending their opponents and self-defense from potential attacks. We consider modeling UAV swarms as biologically inspired mobile adaptive networks to imitate the dynamic confrontations of UAV swarms. It is essential to develop an efficient motion control mechanism underpinned by both target tracking and decision making so as to enhance the capability of UAV swarms. We formulate a distributed modularized framework incorporating collaboratively tracking multiple opponents, making successive and prompt decisions, and controlling motion mechanism for mobile adaptive networks. We develop the diffusion multitask interactive Kalman filter to efficiently track multiple preys (HVTs) and multiple predators (attackers). We further develop the collaborative decision making on prey pursuit or predator evasion such that each UAV would acquire early warnings and predictions of the most hypothetically threatening predators to enhance the effectiveness of self-defense. Moreover, we develop a composite motion control mechanism, such that each UAV would be steered toward the prey or away from any approaching predator in a self-organized manner at a variable speed. The modular design of the proposed framework would promote the affordability and adaptability of UAV swarms. Illustrative simulation results validate the efficacy of the proposed distributed framework and the underlying algorithms.