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<p>To achieve multi-jammer localization in a collaborative jamming attack scenario involving UAVs, this paper designs a multi-jammer iterative localization algorithm based on error minimization and proposes a multi-strategy improved grey wolf optimizer (MSIGWO) algorithm. First, we introduce the network, jamming, and node models under the multi-jammer scenario, as well as the error minimization-based optimization method for localization. This approach transforms the problem into a single-objective optimization problem. Subsequently, we present the standard Grey Wolf Optimizer (GWO) algorithm and propose an improved multi-strategy version tailored to this problem, incorporating nonlinear convergence factors, adaptive differential mutation operators, and chaotic local search strategies to accelerate convergence and avoid falling into local optima. Then, we propose the Multi-Strategy Improved GWO-based and Error Minimization Multi-Jammer Iterative Localization Algorithm (MSIGWO-MJL). Finally, experimental results demonstrate that the proposed method achieves high localization accuracy, accurate estimation of the number of jammers, and excellent overall performance in multi-jammer attack scenarios.</p>

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Iterative localization method for multiple jammers in UAV collaborative jamming attacks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

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  • Li Huang,
  • Luhao Xiong,
  • Shijie Huang,
  • Jingxuan Wang,
  • Changsheng Wan

摘要

To achieve multi-jammer localization in a collaborative jamming attack scenario involving UAVs, this paper designs a multi-jammer iterative localization algorithm based on error minimization and proposes a multi-strategy improved grey wolf optimizer (MSIGWO) algorithm. First, we introduce the network, jamming, and node models under the multi-jammer scenario, as well as the error minimization-based optimization method for localization. This approach transforms the problem into a single-objective optimization problem. Subsequently, we present the standard Grey Wolf Optimizer (GWO) algorithm and propose an improved multi-strategy version tailored to this problem, incorporating nonlinear convergence factors, adaptive differential mutation operators, and chaotic local search strategies to accelerate convergence and avoid falling into local optima. Then, we propose the Multi-Strategy Improved GWO-based and Error Minimization Multi-Jammer Iterative Localization Algorithm (MSIGWO-MJL). Finally, experimental results demonstrate that the proposed method achieves high localization accuracy, accurate estimation of the number of jammers, and excellent overall performance in multi-jammer attack scenarios.