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LI Yihan, SHI Chenguang, LONG Fei, et al. Game theory-based joint optimization algorithm for UAV swarm task allocation and trajectory planning in phantom track deception[J]. Journal of Radars, in press. doi: 10.12000/JR25135
Citation: LI Yihan, SHI Chenguang, LONG Fei, et al. Game theory-based joint optimization algorithm for UAV swarm task allocation and trajectory planning in phantom track deception[J]. Journal of Radars, in press. doi: 10.12000/JR25135
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Game Theory-based Joint Optimization Algorithm for UAV Swarm Task Allocation and Trajectory Planning in Phantom Track Deception

DOI: 10.12000/JR25135 CSTR: 32380.14.JR25135
  • 1.

    Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 2.

    Sichuan Academy of Aerospace Technology, Chengdu 610100, China

Funds:  The National Natural Science Foundation of China (62271247), Natural Science Foundation of Jiangsu Province (BK20240181), National Aerospace Science Foundation of China (20220055052001), Qing Lan Project of Jiangsu Province, Innovation Fund for Distributed Collaborative Systems Engineering and Cluster Technology Research and Development Center (83312202302)
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  • Corresponding author: SHI Chenguang, scg_space@163.com
  • Received Date: 2025-07-23
    Available Online: 2025-09-16
    Abstract
  • To address interruptions in phantom tracks caused by platform failures or damage during unmanned aerial vehicle (UAV) swarm deception operations against radar networks, this study proposes a game theory-based joint optimization algorithm for UAV swarm task allocation and trajectory planning. A decentralized swarm cooperation mechanism is designed to create a cooperative game model for UAV phantom track deception against radar networks. Based on the radar network homology test criterion, an optimization model is developed to maximize the utility function of the phantom track deception game, subject to constraints on UAV swarm kinematic performance and task allocation requirements. The existence and convergence of a Nash equilibrium are rigorously proven using exact potential game theory. To address the resulting non-convex, non-linear, mixed-integer optimization problem, an iterative algorithm is developed that combines distributed coalition game theory with a genetic algorithm. The simulation results demonstrate that, compared with existing approaches, the proposed algorithm effectively replans deception tasks and trajectories in response to platform failures or damage, thereby enhancing the continuity and effectiveness of phantom track generation against radar networks.

     

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