基于群智能的无人机集群侦测计算联合优化算法

洪晖杰; 毛雅淇; 林晓勇; 李大鹏; 赵志翔宇

doi:10.11959/j.issn.1000-0801.2026039

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基于群智能的无人机集群侦测计算联合优化算法

研究与开发 | 更新时间：2026-04-08

- 基于群智能的无人机集群侦测计算联合优化算法
- Joint detection and computation optimization algorithm for UAV swarms based on swarm intelligence
- 电信科学 2026年42卷第3期页码：54-69
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.西北工业大学电子信息学院，陕西西安 710072
- 作者简介：
  
  [ "洪晖杰（2000- ），男，南京邮电大学通信与信息工程学院硕士生，主要研究方向为无人机集群作战、协同计算、边缘计算等。" ]
  [ "毛雅淇（2000- ），女，西北工业大学电子信息学院博士生，主要研究方向为无人机边缘计算等。" ]
  [ "林晓勇（1974- ），男，博士，南京邮电大学通信与信息工程学院副教授，主要研究方向为空天地通信、人工智能等。" ]
  [ "李大鹏（1982- ），男，博士，南京邮电大学通信与信息工程学院教授，主要研究方向为空天地通信、人工智能等。" ]
  [ "赵志翔宇（2005− ），男，南京邮电大学通信与信息工程学院在读，主要研究方向为无人机硬件制作、飞控测试等。" ]
- 基金信息：
  
  国家重点研发计划项目(2021ZD0140405);江苏省自然科学基金资助项目(BK20241885);江苏省大学生科技创新训练计划项目(202510293079Z)
- DOI：10.11959/j.issn.1000-0801.2026039
  中图分类号： TN915.08
- 收稿：2025-07-20，
  
  修回：2025-10-13，
  
  录用：2025-10-17，
  
  纸质出版：2026-03-20
- 稿件说明：
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洪晖杰,毛雅淇,林晓勇等.基于群智能的无人机集群侦测计算联合优化算法[J].电信科学,2026,42(03):54-69.

Hong Huijie,Mao Yaqi,Lin Xiaoyong,et al.Joint detection and computation optimization algorithm for UAV swarms based on swarm intelligence[J].Telecommunications Science,2026,42(03):54-69.
洪晖杰,毛雅淇,林晓勇等.基于群智能的无人机集群侦测计算联合优化算法[J].电信科学,2026,42(03):54-69. DOI： 10.11959/j.issn.1000-0801.2026039.

Hong Huijie,Mao Yaqi,Lin Xiaoyong,et al.Joint detection and computation optimization algorithm for UAV swarms based on swarm intelligence[J].Telecommunications Science,2026,42(03):54-69. DOI： 10.11959/j.issn.1000-0801.2026039.

摘要

无人机（unmanned aerial vehicle，UAV）在灾害预警与战场侦察等任务中发挥着重要作用，但单机模式受限于侦测覆盖与计算能力，难以应对复杂环境。为此，提出一种基于群智能灰狼优化（grey wolf optimization，GWO）的侦测计算联合（joint detection and computation，JDC）优化算法，该算法通过多维度协同优化实现无人机集群侦测与计算能力的双重提升。构建了融合目标存在概率与环境不确定度的动态栅格化侦测模型，以实现自适应侦测部署；设计了基于块坐标下降（block coordinate descent，BCD）算子的空基协同计算框架，以优化任务卸载与资源分配策略。仿真实验结果表明，JDC优化算法可使系统任务时延降低33.35%，收敛速度提升76.47%，在侦算力（effectiveness of detection and computation，EDC）指标上较基准算法提升10.01%～28.74%。实验结果验证了JDC优化算法在无地面基站支持场景下的优越性，为无人机集群自主协同侦察提供了理论支撑与工程指导。

Abstract

Unmanned aerial vehicle (UAV) plays a crucial role in disaster early warning and battlefield reconnaissance

yet single-UAV operation suffers from limited sensing coverage and computational capacity. To overcome these constraints

a joint detection and computation (JDC) optimization algorithm inspired by grey wolf optimization (GWO) within swarm intelligence was proposed. The JDC optimization algorithm integrated a dynamic grid-based detection model

which fused target existence probability and environmental uncertainty for adaptive deployment

with an airborne collaborative computing framework that leveraged distributed task offloading and a block coordinate descent (BCD) operator for efficient resource allocation. The simulation experiment results show that JDC reduces overall latency by 33.35% and achieved 76.47% faster convergence than conventional swarm intelligence methods. The effectiveness of detection and computation (EDC) metric improves by 10.01%～28.74% compared with benchmark algorithms. These results demonstrate the superiority of the proposed JDC optimization algorithm in ground station–denied environments

providing theoretical support for autonomous collaborative surveillance and practical guidance for real-time

high-precision target detection in UAV swarm systems.

关键词

Keywords

references

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