Multi-head graph attention driven feature aggregation for large-scale remote-sening constellation mission planning algorithm

She Yuxuan; Zhou Kangyan; Dai Ao; Wei Zixiang; Zhou Jiaxi; Xiao Lixia

doi:10.11959/j.issn.1000-0801.DXKX250735

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Multi-head graph attention driven feature aggregation for large-scale remote-sening constellation mission planning algorithm

Research and Development | 更新时间：2026-05-13

- Multi-head graph attention driven feature aggregation for large-scale remote-sening constellation mission planning algorithm
- Telecommunications Science Vol. 42, Issue 4, Pages: 31-44(2026)
- 作者机构：
  
  1.华中科技大学网络空间安全学院第六代移动通信研究中心，湖北武汉 430074
  2.西安电子科技大学电子工程学院，陕西西安 710071
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62571202)
- DOI：10.11959/j.issn.1000-0801.DXKX250735
  CLC： TN927.2;TP393
- Received：02 December 2025，
  
  Revised：2026-01-27，
  
  Accepted：28 January 2026，
  
  Published：20 April 2026
- 稿件说明：
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佘雨璇,周康燕,代奥等.多头图注意力驱动特征聚合的巨型遥感星座任务规划算法[J].电信科学,2026,42(04):31-44.

She Yuxuan,Zhou Kangyan,Dai Ao,et al.Multi-head graph attention driven feature aggregation for large-scale remote-sening constellation mission planning algorithm[J].Telecommunications Science,2026,42(04):31-44.
佘雨璇,周康燕,代奥等.多头图注意力驱动特征聚合的巨型遥感星座任务规划算法[J].电信科学,2026,42(04):31-44. DOI： 10.11959/j.issn.1000-0801.DXKX250735.

She Yuxuan,Zhou Kangyan,Dai Ao,et al.Multi-head graph attention driven feature aggregation for large-scale remote-sening constellation mission planning algorithm[J].Telecommunications Science,2026,42(04):31-44. DOI： 10.11959/j.issn.1000-0801.DXKX250735.

摘要

遥感卫星星座规模的持续扩大，极大地增强了其在资源普查、环境监测和灾害应急等领域的综合应用能力。然而，星座规模增长也显著增加了任务规划的复杂性。多智能体强化学习是解决大规模星座任务规划的有效途径，但卫星数量激增也带来智能体状态空间维度爆炸难题。为此，提出一种多头图注意力驱动特征聚合（multi-head graph attention driven feature aggregation，MGADFA）的巨型遥感星座任务规划算法。该方法利用多头图注意力捕捉卫星间的动态交互权重，通过特征聚合机制将复杂的多体交互转化为个体与群体的关联，在保留协作特征的同时，将网络参数规模从指数级降为线性级。仿真结果表明，所提算法在任务接受量和负载均衡性等方面均表现更优。

Abstract

The continuous expansion of remote-sensing satellite constellations has significantly enhanced their comprehensive application capabilities in resource surveys

environmental monitoring

and emergency disaster response. However

the burgeoning scale of these constellations markedly increases the complexity of mission planning. While multi-agent reinforcement learning is an effective approach for large-scale mission planning

the surge in satellite numbers leads to the challenge of dimensionality explosion in the state space. To address this bottleneck

a multi-head graph attention driven feature aggregation (MGADFA) algorithm for mission planning in giant remote-sensing constellations was proposed. This method utilized multi-head graph attention to capture dynamic interaction weights between satellites and employed a feature aggregation mechanism to transform complex multi-body interactions into individual-population associations. While preserving key cooperative features

this approach reduced the joint decision space from an exponential scale to a linear dimension. Simulation results demonstrate that the proposed algorithm exhibites superior performance in terms of task throughput and load balancing.

关键词

Keywords

references

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