空间控制技术与应用（中英文）2026，Vol.52Issue(1)：68-78,11.DOI:10.3969/j.issn.1674-1579.2026.01.007
基于DQN的对地观测卫星调度算法
Earth Observation Satellite Scheduling Based on DQN
摘要
Abstract
The satellite mission planning problem for land resource census is highly challenging,characterized by a high-dimensional nonlinear solution space due to continuously adjustable satellite slewing angles and massive time windows,superimposed with strongly coupled resource constraints.This paper constructs a discrete decision-making model based on"observation opportunities,"decoupling the original problem into two subproblems:observation sequencing and optimal imaging strip selection.To address the limitations of existing algorithms—specifically rule myopia and search inefficiency—when handling such coupled problems involving sequence optimization and parameter selection problems,this paper proposes a scheduling algorithm that integrates variable neighborhood search(VNS)with deep reinforcement learning.This method establishes a hierarchical scheduling model:at the macro level,it utilizes the multiple neighborhood structures of VNS to optimize the observation sequence and escape local optima;at the micro level,it introduces a deep Q-network(DQN)combined with a multi-dimensional state feature space to achieve adaptive evaluation of strip selection values,thereby replacing manually designed rules.Simulation experiments demonstrate that the proposed method exhibits both excellent convergence speed and solution quality,with the gap between the solution score and the theoretical total score being less than 15%for 99%of the test samples.关键词
卫星任务规划/变邻域搜索/深度强化学习Key words
satellite mission planning/variable neighborhood search/deep reinforcement learning分类
航空航天引用本文复制引用
许可,孙昌浩,谢睿达,夏维..基于DQN的对地观测卫星调度算法[J].空间控制技术与应用（中英文）,2026,52(1):68-78,11.基金项目
国家自然科学基金面上项目(72271074 和 62173330)和中央高校基本科研业务费专项资金资助(PA2025GDGP0029) General Program of National Natural Science Foundation of China(72271074 and 62173330)and the Fundamental Research Funds for the Central Universities(PA2025GDGP0029) （72271074 和 62173330）
