| 摘要: |
| 针对多无人机协同干扰组网雷达任务中资源分配导致的干扰效能低下问题,提出一种基于增强型蜣螂优化算法的干扰资源协同优化方法。首先,构建多维干扰资源协同优化模型,涵盖波束指向与功率分配的联合优化；其次,在算法设计层面实施三重改进:采用Tent混沌映射增强种群初始化多样性,改善算法全局探索能力；在偷窃蜣螂位置更新阶段引入柯西-高斯双模变异机制,通过参数自适应调整平衡局部开发精度；构建环境反馈驱动的种群自适应调节机制,基于迭代优化效率动态调整种群结构。实验仿真结果表明本文所提算法能够显著提升算法的收敛速度和全局寻优能力。 |
| 关键词: 无人机协同干扰,蜣螂优化算法,柯西高斯变异,环境反馈机制 |
| DOI： |
| 分类号:TN974 |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| Multi-UAV Cooperative Jamming Strategy Based on Hybrid Strategy Improved DBO |
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| Abstract: |
| Aiming at the problem of low jamming effectiveness caused by resource allocation in multi-UAV cooperative jamming missions against networked radar systems, this paper proposes an enhanced Dung Beetle Optimizer based cooperative optimization method for jamming resource allocation. Firstly, a multi-dimensional cooperative optimization model is constructed, integrating joint optimization of beam steering and power allocation. Secondly, three algorithmic enhancements are implemented: Tent chaotic mapping is adopted to enhance population initialization diversity, improving global exploration capabilities; A Cauchy-Gaussian dual-mode mutation mechanism is introduced during the position update phase of thieving dung beetles, achieving balanced local exploitation precision through parameter self-adaptation; An environmental feedback-driven population adaptation mechanism is established to dynamically adjust population structure based on iterative optimization efficiency. Experimental simulation results demonstrate that the proposed algorithm significantly improves convergence speed and global optimization capability. |
| Key words: UAVs cooperative jamming, dung beetle algorithm, Cauchy-Gaussian mutation, environmental feedback mechanism |