Anti-Jamming Attack Mixed Strategy for Formation Tracking Control via Game-Theoretical Reinforcement Learning

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, we suppose there are three types of UAVs: leaders, followers, and jammers. To illustrate the interactions between multiple UAVs of the formation tracking process under jamming attack, a three-layer Stackelberg game is constructed. Leaders and followers need to resist interference from jammers during the formation tracking process with a leader-follower structure. Leaders and followers achieve formation tracking through cooperation. The interactions between jammers and the other UAVs form a non-cooperative game. Moreover, the utility functions of a mixed strategy, which contain mobility and communication strategies, are designed for the three-layer Stackelberg game. The Stackelberg-Nash equilibrium of the designed game model is proven to exist. For seeking the Stackelberg-Nash equilibrium, a tri-level actor-critic (Tri-AC) reinforcement learning algorithm is designed. The convergence of the designed algorithm is also proved theoretically. Finally, the effectiveness of the designed method is verified by various simulation experiments.

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Publication Details

Journal
IEEE Transactions on Intelligent Vehicles
Published
2024-08-30
DOI
https://doi.org/10.1109/tiv.2024.3452483
Citations
3
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.05
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article

Anti-Jamming Attack Mixed Strategy for Formation Tracking Control via Game-Theoretical Reinforcement Learning

Yongbao Wu, Chaoxu Mu, Bei Ma, Lei Xue et al.
3 citations
IEEE Transactions on Intelligent Vehicles
Distributed Control Multi-Agent Systems
1.05
article

Anti-Jamming Attack Mixed Strategy for Formation Tracking Control via Game-Theoretical Reinforcement Learning

Yongbao Wu, Chaoxu Mu, Bei Ma, Lei Xue, Donald C. Wunsch, Jian Liu
article en
3 citations

Abstract

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, we suppose there are three types of UAVs: leaders, followers, and jammers. To illustrate the interactions between multiple UAVs of the formation tracking process under jamming attack, a three-layer Stackelberg game is constructed. Leaders and followers need to resist interference from jammers during the formation tracking process with a leader-follower structure. Leaders and followers achieve formation tracking through cooperation. The interactions between jammers and the other UAVs form a non-cooperative game. Moreover, the utility functions of a mixed strategy, which contain mobility and communication strategies, are designed for the three-layer Stackelberg game. The Stackelberg-Nash equilibrium of the designed game model is proven to exist. For seeking the Stackelberg-Nash equilibrium, a tri-level actor-critic (Tri-AC) reinforcement learning algorithm is designed. The convergence of the designed algorithm is also proved theoretically. Finally, the effectiveness of the designed method is verified by various simulation experiments.

IEEE Transactions on Intelligent VehiclesVol. 10(5)
Anhui University (CN), Missouri University of Science and Technology (US), Southeast University (CN)
Openalex Percentile: Top 22%
Distributed Control Multi-Agent Systems
1.05
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