A Survey of Cooperative Mission Planning for Imaging Satellites Observing Moving Targets

XU Zhuo; FAN Shenghua; YUE Haitao; QU Tao; WANG Dingwen; SUN Shilei

doi:10.11999/JEIT260133

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XU Zhuo, FAN Shenghua, YUE Haitao, QU Tao, WANG Dingwen, SUN Shilei. A Survey of Cooperative Mission Planning for Imaging Satellites Observing Moving Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260133

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XU Zhuo, FAN Shenghua, YUE Haitao, QU Tao, WANG Dingwen, SUN Shilei. A Survey of Cooperative Mission Planning for Imaging Satellites Observing Moving Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260133

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A Survey of Cooperative Mission Planning for Imaging Satellites Observing Moving Targets

doi: 10.11999/JEIT260133 cstr: 32379.14.JEIT260133

1.
School of Computer Science, Wuhan University, Wuhan 430072, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-29

Available Online: 2026-07-07

Abstract

Abstract

Significance Cooperative mission planning of imaging satellites for moving targets is a key technique for supporting the transition of space-based Earth observation systems from static regional coverage to a dynamic closed-loop mode involving wide-area search, dynamic tracking, and feedback-driven search refinement. It is important for emergency response, maritime monitoring, wide-area situational awareness, and persistent observation of key moving targets. The central difficulty lies in the conflict between the uncertainty of future target states and the dependence of conventional mission planning models on deterministic inputs. Moving targets do not correspond to fixed locations or fixed visibility windows. Their future states usually evolve as probability distributions, confidence regions, or grid-based existence probabilities. Effective planning therefore requires not only predicting target motion, but also transforming uncertainty information into planning objectives, constraints, and replanning triggers. A systematic review from an uncertainty-driven perspective on moving-target observation is therefore still needed. Progress This review summarizes cooperative mission planning of imaging satellites for moving targets from an uncertainty-driven perspective. Typical moving targets are classified into maritime targets, highly time-sensitive aerospace targets, and ground moving targets according to their operating domains, dynamic characteristics, and observation requirements. Although these targets differ in maneuverability, prior constraints, and observation windows, they share a common planning problem: uncertain target motion must be coupled with deterministic satellite observation actions under strict platform and resource constraints. Target motion prediction and spatiotemporal uncertainty representation methods are reviewed. Physics-based methods describe target state evolution through kinematic and dynamic constraints, covariance propagation, reachable regions, or Markov transition models. Data-driven methods learn motion patterns from historical trajectories, Automatic Identification System data, remote sensing observations, meteorological factors, and geographic constraints. From the planning perspective, the value of these methods depends on whether their outputs, such as covariance, existence probability, confidence region, or information gain, can be directly used by planning models. Observation task modeling, cooperative architectures, optimization algorithms, and closed-loop replanning mechanisms are further analyzed. Deterministic simplification models reduce uncertainty into trajectory points, visibility windows, or fixed geographic regions, while probabilistic task modeling incorporates target existence probability, belief state, or information gain into objective functions, constraints, and state transitions. Centralized, distributed, and hybrid architectures are compared in terms of global coordination, onboard autonomy, communication cost, and response timeliness. Exact methods, heuristic rules, metaheuristics, deep reinforcement learning, and large language model (LLM)-enabled solution assistance and algorithm design are discussed. State-triggered replanning, receding horizon optimization, and model predictive control are reviewed as representative mechanisms for closed-loop dynamic scheduling. Conclusions The reviewed studies show that the field is moving from open-loop static scheduling toward closed-loop dynamic planning. However, several bottlenecks remain. First, uncertainty information generated by the prediction layer is not sufficiently used in planning decisions. Rich probabilistic information is often compressed into deterministic time windows, discrete trajectory points, or geometric regions, which weakens risk-sensitive task allocation. Second, distributed cooperation still lacks reliable belief-state consistency. Local observations may lead different satellites to hold inconsistent beliefs about the same target state, causing redundant observations, task conflicts, or unbalanced resource use. Third, dynamic replanning lacks unified benefit-cost criteria for triggering replanning. Frequent replanning may consume excessive attitude maneuvering time, energy, and storage resources, while delayed replanning may miss real target maneuvers. Fourth, LLMs have begun to support task requirement parsing, constraint modeling, heuristic generation, and algorithm design for satellite scheduling, but their use for moving target cooperative planning remains insufficient. Prospects Future research should focus on building a stronger closed-loop planning framework. Prediction uncertainty should be directly used in planning models through chance-constrained planning, belief-state planning, or partially observable Markov decision processes, so that covariance, existence probability, and information entropy can be incorporated into objectives, constraints, and replanning triggers. Bayesian updating or sequential filtering can feed both successful detections and missed detections back to the prediction layer. Lightweight state synchronization and belief fusion are needed for distributed cooperation, supported by compact state-sharing indicators and event-triggered communication. Replanning decisions should be guided by information gain and cost evaluation. Finally, LLMs should be developed as verifiable auxiliary tools rather than direct replacements for optimization solvers. They can support requirement structuring, constraint modeling, heuristic generation, and algorithm component design, while feasibility repair and performance evaluation should be completed by formal checkers, traditional optimizers, and simulation environments. These directions are expected to support robust and uncertainty-aware mission planning for satellite-based moving-target observation.
- Satellite mission planning,
- Multi-satellite collaboration,
- Moving targets,
- Trajectory prediction,
- Onboard intelligence

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References(71)

References

[1]	HALOHO L S and SUPRIYADI A A. Utilization of satellite technology in communication systems, disaster monitoring, border surveillance, and military intelligence: A literature review[J]. Remote Sensing Technology in Defense and Environment, 2024, 1(1): 36–44. doi: 10.61511/rstde.v1i1.2024.842.
[2]	孙伟伟, 杨刚, 陈超, 等. 中国地球观测遥感卫星发展现状及文献分析[J]. 遥感学报, 2020, 24(5): 479–510. doi: 10.11834/jrs.20209464. SUN Weiwei, YANG Gang, CHEN Chao, et al. Development status and literature analysis of China’s earth observation remote sensing satellites[J]. Journal of Remote Sensing, 2020, 24(5): 479–510. doi: 10.11834/jrs.20209464.
[3]	US Congress. FY25 budget request for national security space programs[EB/OL]. https://www.congress.gov/event/118th-congress/house-event/LC75077/text, 2024. (查阅网上资料,不确定文献作者信息,请确认).
[4]	US Congress. H. R. 4107: To improve the missile defense capabilities of the United States, and for other purposes[EB/OL]. https://www.congress.gov/bill/119th-congress/house-bill/4107/text, 2025. (查阅网上资料,不确定文献作者信息,请确认).
[5]	WANG Xinwei, WU Guohua, XING Lining, et al. Agile earth observation satellite scheduling over 20 years: Formulations, methods, and future directions[J]. IEEE Systems Journal, 2021, 15(3): 3881–3892. doi: 10.1109/JSYST.2020.2997050.
[6]	LI Xiutian, CHEN Yingwu, XING Lining, et al. A review of the frameworks, models, and algorithms for large-scale imaging satellite mission planning[J]. Expert Systems with Applications, 2025, 292: 128471. doi: 10.1016/j.eswa.2025.128471.
[7]	SHI Yuanming, ZHU Jingyang, JIANG Chunxiao, et al. Satellite edge artificial intelligence with large models: Architectures and technologies[J]. Science China Information Sciences, 2025, 68(7): 170302. doi: 10.1007/S11432-024-4425-Y.
[8]	CADEMARTORI G, ONETO L, VALDENAZZI F, et al. A review on ship motions and quiescent periods prediction models[J]. Ocean Engineering, 2023, 280: 114822. doi: 10.1016/j.oceaneng.2023.114822.
[9]	LU Wenlong, LIU Bingyan, MU Zhongcheng, et al. Multi-satellite scheduling for stereo tracking of moving targets via parallel island differential evolutionary algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(6): 19194–19214. doi: 10.1109/TAES.2025.3617044.
[10]	SONG Chao, ZHANG Xinyu, SHE Yang, et al. Trajectory planning for UAV swarm tracking moving target based on an improved model predictive control fusion algorithm[J]. IEEE Internet of Things Journal, 2025, 12(12): 19354–19369. doi: 10.1109/JIOT.2025.3541298.
[11]	WEN Xin, LIU Mingmin, and HU Qinglei. Satellite mission planning for moving targets observation via data driven approach[C]. Proceedings of 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019: 2130–2135. doi: 10.23919/ChiCC.2019.8865487.
[12]	温新, 顾玥. 基于数据驱动的移动目标卫星任务规划[J]. 飞控与探测, 2021, 4(3): 15–22. doi: 10.20249/j.cnki.2096-5974.2021.03.003. WEN Xin and GU Yue. Satellite mission planning for moving targets observation via data driven approach[J]. Flight Control & Detection, 2021, 4(3): 15–22. doi: 10.20249/j.cnki.2096-5974.2021.03.003.
[13]	CHENG Zhuo, DENBY B, MCCLEARY K, et al. EagleEye: Nanosatellite constellation design for high-coverage, high-resolution sensing[C]. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '24), La Jolla, USA, 2024: 117–132. doi: 10.1145/3617232.3624851.
[14]	熊韫文, 李毅, 魏才盛. 面向地面移动目标观测的多星成像在线调度方法[J]. 飞控与探测, 2025, 8(5): 34–43. doi: 10.20249/j.cnki.2096-5974.2025.05.004. XIONG Yunwen, LI Yi, and WEI Caisheng. Online imaging scheduling method of multiple satellites for ground moving target observation[J]. Flight Control & Detection, 2025, 8(5): 34–43. doi: 10.20249/j.cnki.2096-5974.2025.05.004.
[15]	HAN Xiaofeng, YANG Ming, WANG Songyan, et al. Continuous monitoring scheduling for moving targets by earth observation satellites[J]. Aerospace Science and Technology, 2023, 140: 108422. doi: 10.1016/j.ast.2023.108422.
[16]	CHU Xiaochen, HAN Xiaofeng, and LI Shuang. Tracking moving targets by earth observation satellites: A multi-objective scheduling approach[C]. Proceedings of 2024 International Conference on New Trends in Computational Intelligence (NTCI), Qingdao, China, 2024: 37–41. doi: 10.1109/NTCI64025.2024.10776156.
[17]	SHI Zhong, ZHAO Fanyu, WANG Xin, et al. Model predictive control-based mission planning method for moving target tracking by multiple observing satellites[C]. Proceedings of the IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 2022: 1158–1162. doi: 10.1109/ITOEC53115.2022.9734412.
[18]	MA Liqi, JIANG Yi, and GUO Zichun. Evaluation of reconnaissance performance of optical satellites for ground-moving-target[C]. Proceedings of the 5th International Conference on Control, Automation and Robotics (ICCAR), Beijing, China, 2019: 861–864. doi: 10.1109/ICCAR.2019.8813312.
[19]	LI Weiming, DU Zhiqiang, WANG Li, et al. Evaluation of the monitoring capabilities of remote sensing satellites for maritime moving targets[J]. ISPRS International Journal of Geo-Information, 2024, 13(9): 325. doi: 10.3390/IJGI13090325.
[20]	慈元卓, 贺仁杰, 徐一帆, 等. 卫星搜索移动目标问题中的目标运动预测方法研究[J]. 控制与决策, 2009, 24(7): 1007–1012. doi: 10.13195/j.cd.2009.07.49.ciyzh.008. CI Yuanzhuo, HE Renjie, XU Yifan, et al. Method of target motion prediction for moving target search by satellite[J]. Control and Decision, 2009, 24(7): 1007–1012. doi: 10.13195/j.cd.2009.07.49.ciyzh.008.
[21]	冉承新, 王慧林, 熊纲要, 等. 基于改进遗传算法的移动目标成像侦测任务规划问题研究[J]. 宇航学报, 2010, 31(2): 457–465. doi: 10.3873/j.issn.1000-1328.2010.02.024. RAN Chengxin, WANG Huilin, XIONG Gangyao, et al. Research on mission-planning of ocean moving targets imaging reconnaissance based on improved genetic algorithm[J]. Journal of Astronautics, 2010, 31(2): 457–465. doi: 10.3873/j.issn.1000-1328.2010.02.024.
[22]	王慧林, 邱涤珊, 马满好, 等. 基于先验信息的海洋移动目标卫星成像侦测任务规划[J]. 火力与指挥控制, 2011, 36(3): 105–110. doi: 10.3969/j.issn.1002-0640.2011.03.028. WANG Huilin, QIU Dishan, MA Manhao, et al. Research on mission-planning of satellite imaging reconnaissance for ocean moving targets based on the prior information[J]. Fire Control & Command Control, 2011, 36(3): 105–110. doi: 10.3969/j.issn.1002-0640.2011.03.028.
[23]	MEI Guanlin, RAN Xiaomin, FANG Deliang, et al. Improved satellite scheduling algorithm for moving target[C]. Proceedings of the 4th International Conference on Information Science and Cloud Computing (ISCC2015), Guangzhou, China, 2015: 18–19. doi: 10.22323/1.264.0058.
[24]	YU Tianyue, ZHANG Yasheng, and YANG Jie. Study on the fast search planning problem of lost targets for maritime emergency response based on an improved adaptive immunogenetic algorithm[J]. Sensors, 2024, 24(12): 3904. doi: 10.3390/S24123904.
[25]	REN Xupu, CHENG Yao, and LI Yuqing. A method for continuous observation of sea surface moving targets based on multi point trajectory prediction[J]. Journal of Physics: Conference Series, 2024, 2762(1): 012059. doi: 10.1088/1742-6596/2762/1/012059.
[26]	WANG Yao, LUO Junren, GU Xueqiang, et al. Research on the reconfiguration method of space-based exploration satellite constellations for moving target tracking at sea[J]. Applied Sciences, 2023, 13(18): 10103. doi: 10.3390/APP131810103.
[27]	张海龙, 夏维, 胡笑旋, 等. 面向多障碍物海面卫星搜索动目标方法[J]. 系统工程与电子技术, 2018, 40(10): 2256–2262. doi: 10.3969/j.issn.1001-506X.2018.10.15. ZHANG Hailong, XIA Wei, HU Xiaoxuan, et al. Method for moving targets search by satellites on multi-obstacle sea[J]. Systems Engineering and Electronics, 2018, 40(10): 2256–2262. doi: 10.3969/j.issn.1001-506X.2018.10.15.
[28]	CAO Xibin, LI Ning, QIU Shi, et al. Research on the method of searching and tracking of the time-sensitive target through the mega-constellation[J]. Aerospace Science and Technology, 2023, 137: 108299. doi: 10.1016/j.ast.2023.108299.
[29]	LU Wenlong, GAO Weihua, LIU Bingyan, et al. Reinforcement learning driven time-sensitive moving target tracking of intelligent agile satellite[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(6): 9085–9101. doi: 10.1109/TAES.2024.3436061.
[30]	YANG Jie, YU Tianyue, and ZHANG Yasheng. Search planning problem for lost targets at sea based on historical trajectory information[C]. Proceedings of the 5th International Symposium on Computer Technology and Information Science (ISCTIS), Xi'an, China, 2025: 703–711. doi: 10.1109/ISCTIS65944.2025.11065938.
[31]	ZHENG Xiao, PENG Xiaodong, GUO Zhiyuan, et al. A position probability prediction method of marine moving targets for optimization search by satellite[C]. Proceedings of 2024 IEEE International Conference on Control Science and Systems Engineering (ICCSSE), Beijing, China, 2024: 55–62. doi: 10.1109/ICCSSE63803.2024.10823961.
[32]	XIE Zhiye, TU Enmei, FU Xianping, et al. AIS data-driven maritime monitoring based on transformer: A comprehensive review[C]. Proceedings of 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025: 1–8. doi: 10.1109/IJCNN64981.2025.11228006.
[33]	崔亚奇, 徐平亮, 龚诚, 等. 基于全球AIS的多源航迹关联数据集[J]. 电子与信息学报, 2023, 45(2): 746–756. doi: 10.11999/JEIT221202. CUI Yaqi, XU Pingliang, GONG Cheng, et al. Multisource track association dataset based on the global AIS[J]. Journal of Electronics & Information Technology, 2023, 45(2): 746–756. doi: 10.11999/JEIT221202.
[34]	LIU Da, ZONG Qun, ZHANG Xiuyun, et al. Enhancing space-based situational awareness: Real-time observation of dynamic targets with meta-cooperative-scheduling net[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(6): 8198–8211. doi: 10.1109/TAES.2024.3425389.
[35]	CHI Yu, LI Zhaoyu, XU Rui, et al. Improving genetic task planning method for observing moving targets with dual-satellite[J]. IFAC-PapersOnLine, 2025, 59(20): 1912–1917. doi: 10.1016/J.IFACOL.2025.11.437.
[36]	LIU Yilong, ZHANG Cong, ZHANG Sihang, et al. Multi-satellite mission planning method for dynamic targets based on reinforcement learning[C]. Proceedings of 2023 China Automation Congress (CAC), Chongqing, China, 2023: 3865–3870. doi: 10.1109/CAC59555.2023.10450405.
[37]	LIU Yan, WEN Zhijiang, ZHANG Shengyu, et al. Learning-based constellation scheduling for time-sensitive space multi-target collaborative observation[J]. Advances in Space Research, 2024, 73(9): 4751–4766. doi: 10.1016/J.ASR.2024.02.013.
[38]	MORGAN S J, MCGRATH C N, and DE WECK O L. Optimization of multispacecraft maneuvers for mobile target tracking from low earth orbit[J]. Journal of Spacecraft and Rockets, 2023, 60(2): 581–590. doi: 10.2514/1.A35457.
[39]	LIU Dacheng, CHANG Sheng, DENG Yunkai, et al. A novel spaceborne SAR constellation scheduling algorithm for sea surface moving target search tasks[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 3715–3726. doi: 10.1109/JSTARS.2024.3355974.
[40]	QIN Jiahao, BAI Xue, DU Guoming, et al. Multisatellite scheduling for moving targets using the enhanced hybrid genetic simulated annealing algorithm and observation strip selection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(5): 5773–5800. doi: 10.1109/TAES.2024.3397958.
[41]	QI Maochen, GUO Wenting, LIU Zhengyang, et al. Agile satellite mission planning for moving targets observation based on modified genetic algorithm[C]. Proceedings of the 44th Chinese Control Conference (CCC), Chongqing, China, 2025: 2001–2006. doi: 10.23919/CCC64809.2025.11178330.
[42]	杨迪, 李振瑜, 郭帅, 等. 天基低轨海上移动目标成像搜索任务调度[J]. 航空学报, 2023, 44(15): 528752. doi: 10.7527/S1000-6893.2023.28752. YANG Di, LI Zhenyu, GUO Shuai, et al. Space-based LEO-observation search planning for maritime moving targets[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(15): 528752. doi: 10.7527/S1000-6893.2023.28752.
[43]	NAGANO Y and SCHAUB H. Autonomous task scheduling for earth-observing satellites tracking moving targets with low maneuverability[C]. AIAA SCITECH 2026 Forum, Orlando, USA, 2026: 1387. doi: 10.2514/6.2026-1387.
[44]	HU Jiaxin, ZHAO Demin, and ZHU Yanwei. A Dynamic Mission Planning Method of Multi-Satellite Cooperative Observation for Highly Time-Sensitive Targets[M]. Amsterdam: IOS Press, 2025: 336–342. doi: 10.3233/ATDE250060. (查阅网上资料,未找到本条文献出版地信息,请确认).
[45]	CUI Jintian and ZHANG Xin. Application of a multi-satellite dynamic mission scheduling model based on mission priority in emergency response[J]. Sensors, 2019, 19(6): 1430. doi: 10.3390/s19061430.
[46]	LI Meicheng, FENG Xiaoen, XU Minqiang, et al. Multilevel guided collaborative task scheduling algorithm of satellite mission aiming at moving target observation[C]. Proceedings of SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024), Wuhan, China, 2024: 1339545. doi: 10.1117/12.3049831.
[47]	WU Qianyu, PAN Jun, and WANG Mi. Dynamic task planning method for multi-source remote sensing satellite cooperative observation in complex scenarios[J]. Remote Sensing, 2024, 16(4): 657. doi: 10.3390/RS16040657.
[48]	CAO Yanjun, LIN Xiaoyong, CHEN Zhanhua, et al. Research on methods for analysis and planning of complex multi-satellite missions[C]. Proceedings of the 3rd International Symposium on Aerospace Engineering and Systems (ISAES), Nanjing, China, 2024: 306–311. doi: 10.1109/ISAES61964.2024.10751656.
[49]	简平, 邹鹏, 熊伟. 低轨预警系统动态任务规划启发式算法[J]. 电子与信息学报, 2013, 35(10): 2438–2444. doi: 10.3724/SP.J.1146.2013.00072. JIAN Ping, ZOU Peng, and XIONG Wei. Heuristic algorithm for dynamic task planning of early warning system of low earth orbit[J]. Journal of Electronics & Information Technology, 2013, 35(10): 2438–2444. doi: 10.3724/SP.J.1146.2013.00072.
[50]	YAO Jiali, XU Bo, LI Xin, et al. A clustering scheduling strategy for space debris tracking[J]. Aerospace Science and Technology, 2025, 157: 109805. doi: 10.1016/j.ast.2024.109805.
[51]	LU Wenglong, GAO Weihua, LIU Bingyan, et al. Parallel dual adaptive genetic algorithm: A method for satellite constellation task assignment in time-sensitive target tracking[J]. Advances in Space Research, 2024, 74(10): 5192–5213. doi: 10.1016/J.ASR.2024.07.044.
[52]	XIANG Shang, WANG ling, XING Lining, et al. Knowledge-based memetic algorithm for joint task planning of multi-platform earth observation system[J]. Computers & Industrial Engineering, 2021, 160: 107559. doi: 10.1016/J.CIE.2021.107559.
[53]	CHAO Tao, HAN Xiaofeng, LI Xiang, et al. Multi-objective optimization of continuous monitoring scheduling for moving targets by earth observation satellites[J]. Engineering Applications of Artificial Intelligence, 2025, 144: 110056. doi: 10.1016/J.ENGAPPAI.2025.110056.
[54]	LI Xiang, HAN Xiaofeng, MA Ping, et al. Mission planning of continuous tracking moving targets by earth observation satellite in unknown scenarios[C]. Proceedings of the IEEE 19th International Conference on Control & Automation (ICCA), Tallinn, Estonia, 2025: 244–249. doi: 10.1109/ICCA65672.2025.11129799.
[55]	CONG Yiqin, MEI Xiaohan, SUN Shengxin, et al. Autonomous collaborative observation method for time-sensitive moving target tracking by satellite swarms[J]. Advances in Space Research, 2025, 75(7): 5615–5629. doi: 10.1016/j.asr.2025.01.012.
[56]	SHI Zhong, JIN Zhonghe, and WANG Huiquan. Satellite attitude tracking decision method based on deep deterministic policy gradient for moving target observation[C]. Proceedings of the 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021: 868–872. doi: 10.1109/IAEAC50856.2021.9390829.
[57]	LIANG Zhihua, DENG Wudong, and DONG Yunfeng. A logical dimensional reinforcement learning approach for component-level collaborative planning in cluster satellites[J]. Acta Astronautica, 2026, 241: 575–593. doi: 10.1016/j.actaastro.2026.01.021.
[58]	WANG Feiran, CHEN Jiawei, DU Yonghao, et al. LLM-assisted adaptive large neighborhood search for agile earth observation satellite scheduling[J]. Engineering Management, 2026, 13(1): 213–239. doi: 10.1007/s42524-026-5124-4.
[59]	CHEN Jiawei, CHEN Yingguo, PHAM D T, et al. A large language model-based multi-agent framework to autonomously design algorithms for earth observation satellite scheduling problem[J]. Engineering, 2025. doi: 10.1016/j.eng.2025.10.027.
[60]	SHI Hongxi, DU Yonghao, ZHANG Ziyang, et al. LLM based bi-level online order dispatching and scheduling for large-scale earth observation satellites[C]. Proceedings of 2025 IEEE Congress on Evolutionary Computation (CEC), Hangzhou, China, 2025: 1–4. doi: 10.1109/CEC65147.2025.11043078.
[61]	CHEN Jiawei, PEDRYCZ W, WANG Feiran, et al. A tri-stage LLM-coordinated framework for order-driven scheduling of earth observation satellite tasks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2026, 64: 3002015. doi: 10.1109/TGRS.2026.3700605.
[62]	LIU Fei, YAO Yiming, GUO Ping, et al. A systematic survey on large language models for algorithm design[J]. ACM Computing Surveys, 2026, 58(8): 218. doi: 10.1145/3787585.
[63]	WU Xingyu, WU Shenghao, WU Jibin, et al. Evolutionary computation in the era of large language model: Survey and roadmap[J]. IEEE Transactions on Evolutionary Computation, 2025, 29(2): 534–554. doi: 10.1109/TEVC.2024.3506731.
[64]	JIANG Yang, GAO Yuan, YU Longjiang, et al. Self-organizing method on mission-level task allocation of large-scale remote sensing satellite swarm[J]. International Journal of Aerospace Engineering, 2022, 2022(1): 9307837. doi: 10.1155/2022/9307837.
[65]	李宗凌, 龙腾, 赵保军, 等. 面向预警场景的大规模星座协同调度标准建模与求解方法[J]. 航空学报, 2024, 45(22): 330181. doi: 10.7527/S1000-6893.2024.30181. LI Zongling, LONG Teng, ZHAO Baojun, et al. Standard modeling and solving methods for large-scale constellation collaborative scheduling for early warning scenarios[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(22): 330181. doi: 10.7527/S1000-6893.2024.30181.
[66]	CHEN Hao, PENG Shuang, DU Chun, et al. Distributed satellite task scheduling models and methods[M]. CHEN Hao, PENG Shuang, DU Chun, et al. Earth Observation Satellites: Task Planning and Scheduling. Singapore: Springer Nature Singapore, 2023: 111–132. doi: 10.1007/978-981-99-3565-9_5.
[67]	杜永浩, 张本奎, 吴健, 等. 大规模遥感卫星智能任务调度方法研究进展[J]. 电子与信息学报, 2025, 47(12): 5033–5047. doi: 10.11999/JEIT251038. DU Yonghao, ZHANG Benkui, WU Jian, et al. Survey on intelligent methods for large-scale remote sensing satellite scheduling[J]. Journal of Electronics & Information Technology, 2025, 47(12): 5033–5047. doi: 10.11999/JEIT251038.
[68]	ZHANG Chao, CHEN Jinyong, LI Yanbin, et al. Satellite group autonomous operation mechanism and planning algorithm for marine target surveillance[J]. Chinese Journal of Aeronautics, 2019, 32(4): 991–998. doi: 10.1016/j.cja.2019.02.005.
[69]	夏维, 魏宏图, 程颖, 等. 面向卫星任务规划的专家链构建与优化方法[J]. 电子与信息学报, 2025, 47(12): 4986–4994. doi: 10.11999/JEIT251018. XIA Wei, WEI Hongtu, CHENG Ying, et al. An expert chain construction and optimization method for satellite mission planning[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4986–4994. doi: 10.11999/JEIT251018.
[70]	陈盈果, 王斐然, 胡云鹏, 等. 融合大语言模型与强化学习的敏捷卫星任务分配算法设计[J]. 电子与信息学报, 2025, 47(12): 4959–4972. doi: 10.11999/JEIT250991. CHEN Yingguo, WANG Feiran, HU Yunpeng, et al. Automating algorithm design for agile satellite task assignment with large language models and reinforcement learning[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4959–4972. doi: 10.11999/JEIT250991.
[71]	CHEN Nanyu, YANG Anran, WU Hui, et al. SEMINT: An LLM-empowered long-term vessel trajectory prediction framework[J]. International Journal of Geographical Information Science, 2025, 39(9): 1938–1972. doi: 10.1080/13658816.2025.2487990.