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| 无人机编队协同制导方法研究 |
| Research on Cooperative Guidance Method of UAV Formation |
| 投稿时间：2026-08-04 修订日期：2026-10-19 |
| DOI： |
| 中文关键词: 无人机 编队协同制导 目标分配 蚁群算法 灰狼优化算法 时间协同 |
| English Keywords:UAV formation cooperative guidance target allocation ant colony algorithm grey wolf optimization algorithm time cooperation |
| 基金项目:国家自然科学基金资助项目 |
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| 摘要点击次数: 5 |
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| 中文摘要: |
| 针对无人机编队协同打击多目标的需求，提出从目标分配到末端制导的全链路协同制导方法。建立了融合攻击角度、相对距离、过载优势度与目标威胁度的综合攻击性能评估模型，构建了攻防一体效费比指标，将目标分配建模为带约束整数规划问题并采用蚁群算法求解。采用栅格法建模战场环境，提出了基于改进灰狼优化算法的协同航迹规划方法。建立了长机-僚机机目相对运动模型，设计了含附加机动控制指令的时间协同制导律。仿真结果表明，6架无人机的最大命中时间差由纯比例导引下的41.1s缩减至协同制导下的3.4s，实现了多机多目标的同时打击。 |
| English Summary: |
| Aiming at the cooperative strike of UAV formation against multiple targets, a full-link cooperative guidance method from target allocation to terminal guidance is proposed. A comprehensive attack performance evaluation model integrating attack angle, relative distance and overload superiority with target threat is established, and an attack-defense integrated cost-effectiveness index is constructed. The target allocation is modeled as a constrained integer programming problem and solved by the ant colony algorithm. The grid method is used to model the battlefield environment, and a cooperative path planning method based on the improved grey wolf optimization algorithm is proposed. A leader-wingman relative motion model is established, and a time-cooperative guidance law with additional maneuver commands is designed to achieve simultaneous arrival of multiple UAVs. Numerical simulation results show that the ant colony algorithm converges stably to the global optimal allocation scheme within a few generations, the improved grey wolf algorithm outperforms the standard one in both convergence speed and solution quality, and the maximum impact time dispersion of six UAVs is reduced from 41.1 s to 3.4 s, which verifies the effectiveness of the proposed method for simultaneous multi-UAV multi-target strike. |
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