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<p>In modern warfare, decoy targets are often set up by the enemy to interfere with the combat of unmanned aerial vehicle (UAV) swarms. To enhance the combat effectiveness of UAV swarm collaborative target assignment and improve its anti-interference ability, we model this problem as a deletion-robust submodular maximization problem with a knapsack constraint. We provide a two-stage framework consisting of a preprocessing algorithm and postprocessing algorithms. Simulation experiments are carried out through the weapon allocation problem and the UAV swarm collaborative target assignment problem. The results show that the proposed algorithms perform better than non-deletion-robust algorithms in terms of objective function values and running time, verifying the effectiveness of the algorithms.</p>

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Defeating decoys: deletion-robust submodular optimization for UAV swarm target assignment problem

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Jiaming Hu,
  • Chuangbo Hao,
  • Mengzhen Li,
  • Yuhao Wang,
  • Maowen Lu,
  • Dachuan Xu

摘要

In modern warfare, decoy targets are often set up by the enemy to interfere with the combat of unmanned aerial vehicle (UAV) swarms. To enhance the combat effectiveness of UAV swarm collaborative target assignment and improve its anti-interference ability, we model this problem as a deletion-robust submodular maximization problem with a knapsack constraint. We provide a two-stage framework consisting of a preprocessing algorithm and postprocessing algorithms. Simulation experiments are carried out through the weapon allocation problem and the UAV swarm collaborative target assignment problem. The results show that the proposed algorithms perform better than non-deletion-robust algorithms in terms of objective function values and running time, verifying the effectiveness of the algorithms.