Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling

DU Yonghao; ZHANG Benkui; WU Jian; CHEN Yingguo; YAN Donglei; YU Haiyan; XING Lining; BAI Baocun

doi:10.11999/JEIT251038

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DU Yonghao, ZHANG Benkui, WU Jian, CHEN Yingguo, YAN Donglei, YU Haiyan, XING Lining, BAI Baocun. Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251038

Citation:

DU Yonghao, ZHANG Benkui, WU Jian, CHEN Yingguo, YAN Donglei, YU Haiyan, XING Lining, BAI Baocun. Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251038

Citation:

DU Yonghao, ZHANG Benkui, WU Jian, CHEN Yingguo, YAN Donglei, YU Haiyan, XING Lining, BAI Baocun. Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251038

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Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling

doi: 10.11999/JEIT251038 cstr: 32379.14.JEIT251038

DU Yonghao¹,
ZHANG Benkui²,
WU Jian^{3
,
,},
CHEN Yingguo¹,
YAN Donglei^{4, 5},
YU Haiyan^{5, 8},
XING Lining⁶,
BAI Baocun⁷

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2.
Aerospace Information Innovation Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
4.
Space Star Technology Co., Ltd., Beijing 100086, China
5.
China Academy of Space Technology, Beijing 100094, China
6.
School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
7.
Project Management Center, Beijing 100000, China
8.
Qian Xuesen Space Technology Laboratory, Beijing 100094, China

Funds: The National Natural Science Foundation of China (72201272,72501214,72201273), Young Elite Scientists Sponsorship Program by CAST(2023-JCJQ-QT-042), The Science and Technology Innovation Program of Hunan Province (2025RC3111)

Received Date: 2025-09-30
Rev Recd Date: 2026-01-09

Available Online: 2026-01-13

Abstract

Abstract

Significance Satellite task scheduling is an operational optimization technique. It constructs combinatorial optimization models for space–ground resources and applies operations research and computational intelligence methods to generate task plans, resolve task conflicts and constraints, and maximize satellite utilization efficiency. With the development of large-scale constellations, satellite task scheduling faces several new challenges. (1) The rapid increase in the number of satellites and tasks leads to a combinatorial explosion of the solution space. (2) Satellite applications are shifting from planned operations to on-demand services, which require response times to be reduced from hours to minutes or even seconds. (3) Advances in satellite payload capabilities enable onboard autonomous decision making and in-orbit collaboration, which support interactive and swarm-intelligence-based management of large-scale remote sensing constellations. Progress To address large-scale complexity, constellation collaboration, and on-demand service requirements in satellite task scheduling, recent research developments are reviewed from the perspectives of task scheduling frameworks, task scheduling models, and task scheduling algorithms, following a top-down approach. First, centralized scheduling frameworks, distributed scheduling frameworks, and hybrid centralized–distributed scheduling frameworks are described, and their control paradigms and application scenarios are clarified. Second, task scheduling models are examined according to their theoretical foundations and applicable solution methods, including classical operations research models, constraint satisfaction optimization models, and artificial neural network-based decision-making models. Their modeling approaches and application scopes are discussed in detail. Subsequently, three major classes of task scheduling algorithms are summarized, including exact algorithms, metaheuristic algorithms, and machine learning-based algorithms. Their decision-making mechanisms, advantages, and limitations are analyzed. Finally, future research directions are identified, including the reconstruction of large-scale and order-oriented task scheduling frameworks, the development of novel task scheduling models, and the innovative integration of different task scheduling algorithms. Conclusions and prospects At the framework level, task scheduling frameworks for constellations consisting of more than one thousand satellites have not yet been reported. Existing task scheduling frameworks mainly address problems with fewer than 100 satellites, which remains insufficient for large-scale remote sensing constellations with thousands or even tens of thousands of satellites. The hybrid centralized–distributed task scheduling framework combines the advantages of centralized scheduling frameworks and distributed scheduling frameworks and is consistent with the hierarchical construction and management characteristics of satellite constellations. It can further adapt to satellite scale expansion and order-based process mechanisms. At the model level, constraint satisfaction optimization models focus on detailed representations of optimization attributes and elements and are suitable for small-scale and medium-scale satellite task scheduling problems. In contrast, artificial neural network-based decision-making models emphasize classification and decision-making characteristics and support online and on-demand scheduling, which makes them suitable for large-scale satellite task scheduling scenarios. These two types of task scheduling models can therefore be coordinated to characterize different stages of large-scale constellation task scheduling. At the algorithm level, the integration of metaheuristic algorithms and machine learning-based algorithms has become an important technical approach for solving satellite task scheduling problems. This integrated approach supports hybrid centralized–distributed task scheduling frameworks and complements both constraint satisfaction optimization models and artificial neural network-based decision-making models.
- Remote sensing satellite task scheduling,
- Task scheduling framework,
- Task scheduling model,
- Task scheduling algorithm,
- Intelligent optimization method

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References(100)

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