干扰环境下基于博弈论的无人机群部署与组网方法

该文研究了干扰环境下基于博弈论的无人机(UAV)群部署与组网方法。首先，基于拥塞博弈，提出一种分布式无人机群部署算法(CUD)。每架无人机可以通过与邻近无人机的有限交互，实现自主的位置优化，以提高数据采集量，并增强干扰躲避能力。其次，基于联盟形成博弈，提出一种无人机群动态组网算法(USACF)，可使无人机群在干扰威胁下实现分布式动态子网形成，提高数据传输质量，并增强无人机网络的鲁棒性和可靠性。此外，借助精确势能博弈，从理论上证明了所提博弈模型可以获得稳定的纳什均衡解。最后，仿真结果表明，所提算法相较于传统算法有明显的性能提升。

Deployment and Networking Methods of UAV Swarm in Jamming Environments Based on Game Theory

A deployment and networking methods of Unmanned Aerial Vehicle (UAV) swarm based on game theory in the jamming environments is investigated in this paper. Firstly, a Congestion-game based UAV swarm Deployment algorithm (CUD）is proposed. Each UAV can autonomously optimize its position through limited interaction with adjacent UAVs to increase the amount of collected data and enhance the anti-jamming capabilities. Secondly, a UAV Swarm Anti-jamming Coalition Formation algorithm (USACF) is proposed, which enables the UAV swarm to form dynamic sub-networks in a distributed way under the threat of hostile jamming, thus improving the transmission performance and enhancing the robustness and reliability of the UAV networks. Furthermore, it is proved theoretically that the proposed game model can achieve a stable Nash equilibrium with the aid of exact potential game theory. Finally, simulation results verify that the proposed algorithms have obvious performance improvement compared with the conventional algorithms.