Electronics and Electrical Engineering and Control

Grid subdivision-based agile satellite scheduling method for observing area targets

  • Guohua WU ,
  • Xinyang ZHOU ,
  • Yi GU ,
  • Wei TANG
Expand
  • 1.College of Automation,Central South University,Changsha 410083,China
    2.School of Traffic and Transportation Engineering,Central South University,Changsha 410083,China
    3.Center for Grey Systems Studies,Northwestern Polytechnical University,Xi’an 710072,China
E-mail: yi.gu@csu.edu.cn

Received date: 2025-08-27

  Revised date: 2025-11-01

  Accepted date: 2025-12-02

  Online published: 2025-12-23

Supported by

National Natural Science Foundation of China(62503503);Changsha Municipal Natural Science Foundation(kq2502018)

Abstract

Agile satellites possess roll and pitch maneuver capabilities, and adopting the strip-splicing mode for area target observation can improve the coverage rate. However, this observation process requires simultaneous optimization of roll angles, pitch angles, and the observation start and end times of each strip. To address this multi-strip observation scheduling problem, an agile satellite area target observation scheduling method based on grid subdivision is proposed. The method employs hexagonal grids to perform a double-layer discretization of the target area, which is utilized for attitude mapping, coverage calculation, and heuristic guidance. With the goal of maximizing the coverage rate, a mixed-integer nonlinear programming model considering multiple constraints is established. To solve the model, a bilevel optimization framework based on adaptive feedback iteration is designed, and a bilevel optimization algorithm integrating simulated annealing with cooperative particle swarm genetic algorithm is constructed. Specifically, the upper layer performs observation strip allocation via simulated annealing and its multi-neighborhood search strategy, while the lower layer employs the cooperative particle swarm genetic algorithm to schedule pitch angles and observation windows. And the interactive iterative solution of the upper and lower layer algorithms is realized through the adaptive feedback mechanism. Extensive comparative experiments demonstrate that in agile satellite area target observation scenarios, the proposed method is superior to various comparison algorithms in terms of coverage rate and convergence stability. Moreover, compared with the traditional single-strip Earth observation mode, the strip-splicing mode of agile satellites can significantly improve the area coverage rate.

Cite this article

Guohua WU , Xinyang ZHOU , Yi GU , Wei TANG . Grid subdivision-based agile satellite scheduling method for observing area targets[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(14) : 332713 -332713 . DOI: 10.7527/S1000-6893.2025.32713

References

[1] YIN Q, WU G H, SUN G, et al. Multi-objective orbital maneuver optimization of multi-satellite using an adaptive feedback learning NSGA-Ⅱ[J]. Swarm and Evolutionary Computation202593: 101835.
[2] WU G H, XIANG Z Q, WANG Y L, et al. Improved adaptive large neighborhood search algorithm based on the two-stage framework for scheduling multiple super-agile satellites[J]. IEEE Transactions on Aerospace and Electronic Systems202460(5): 7185-7200.
[3] GU Y, HAN C, CHEN Y H, et al. Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization[J]. IEEE Transactions on Aerospace and Electronic Systems202359(2): 1800-1815.
[4] KANDEPI R, SAINI H, GEORGE R K, et al. Agile Earth observation satellite constellations scheduling for large area target imaging using heuristic search[J]. Acta Astronautica2024219: 670-677.
[5] 伍国华, 王天宇. 基于自适应模拟退火的大规模星座测控资源调度算法[J]. 航空学报202344(12): 327759.
  WU G H, WANG T Y. Large-scale constellation TT&C resource scheduling algorithm based on adaptive simulated annealing[J]. Acta Aeronautica et Astronautica Sinica202344(12): 327759 (in Chinese).
[6] EDDY D, KOCHENDERFER M. Markov decision processes for multi-objective satellite task planning[C]∥2020 IEEE Aerospace Conference. Piscataway: IEEE Press, 2020: 1-12.
[7] WOLFE W J, SORENSEN S E. Three scheduling algorithms applied to the Earth observing systems domain[J]. Management Science200046(1): 148-166.
[8] WANG M C, PEI C, CHEN X Y, et al. A dynamic decomposition optimization framework for multi-satellite scheduling of area target observation[J]. Advances in Space Research202575(6): 4664-4681.
[9] CORDONE R, GANDELLINI F, RIGHINI G. Solving the swath segment selection problem through Lagrangean relaxation[J]. Computers & Operations Research200835(3): 854-862.
[10] HU X X, ZHU W M, MA H W, et al. Orientational variable-length strip covering problem: A branch-and-price-based algorithm[J]. European Journal of Operational Research2021289(1): 254-269.
[11] WU G H, LIU J, MA M H, et al. A two-phase scheduling method with the consideration of task clustering for Earth observing satellites[J]. Computers and Operations Research201340(7): 1884-1894.
[12] WU X D, YANG Y H, XIE Y E, et al. Multiregion mission planning by satellite swarm using simulated annealing and neighborhood search[J]. IEEE Transactions on Aerospace and Electronic Systems202460(2): 1416-1439.
[13] LI F, WAN Q H, WEN F F, et al. Multi-satellite imaging task planning for large regional coverage: A heuristic algorithm based on triple grids method[J]. Remote Sensing202416(1): 194.
[14] WU X D, YANG Y H, SUN Y Q, et al. Dynamic regional splitting planning of remote sensing satellite swarm using parallel genetic PSO algorithm[J]. Acta Astronautica2023204: 531-551.
[15] 顾轶, 王慧林, 郭鹤鹤, 等. 基于视场映射模型的GEO目标观测覆盖性分析[J]. 航空学报202546(15): 331570.
  GU Y, WANG H L, GUO H H, et al. Analysis of GEO target observation coverage based on field of view mapping model[J]. Acta Aeronautica et Astronautica Sinica202546(15): 331570 (in Chinese).
[16] 顾轶, 孙秀聪, 范黎明, 等. 基于仰角视元模型的星地快速覆盖分析方法[J]. 航空学报202445(23): 330372.
  GU Y, SUN X C, FAN L M, et al. A rapid satellite-ground coverage analysis method based on elevation view element model[J]. Acta Aeronautica et Astronautica Sinica202445(23): 330372 (in Chinese).
[17] 李恒伟, 罗启章, 顾轶, 等. 基于滚动时域策略的中继卫星多目标动态调度优化方法[J]. 航空学报202445(16): 329706.
  LI H W, LUO Q Z, GU Y, et al. Multi-objective dynamic scheduling optimization method for relay satellites based on rolling horizon strategy[J]. Acta Aeronautica et Astronautica Sinica202445(16): 329706 (in Chinese).
[18] WANG X W, WU G H, XING L N, et al. Agile Earth observation satellite scheduling over 20 years: Formulations, methods, and future directions[J]. IEEE Systems Journal202115(3): 3881-3892.
[19] LEMA?? TRE M, VERFAILLIE G, JOUHAUD F, et al. Selecting and scheduling observations of agile satellites[J]. Aerospace Science and Technology20026(5): 367-381.
[20] 杜彬. 敏捷成像卫星任务规划方法研究[D]. 南京: 南京航空航天大学, 2019: 41-60.
  DU B. Research on agile imaging satellite mission planning method[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019: 41-60 (in Chinese).
[21] 宋金运, 刘翔春, 冯宁, 等. 面向区域目标的敏捷成像卫星单轨调度方法[J]. 数字技术与应用202341(9): 81-83.
  SONG J Y, LIU X C, FENG N, et al. Single-track scheduling method of agile imaging satellite for regional target[J]. Digital Technology & Application202341(9): 81-83 (in Chinese).
[22] 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202445(22): 330181.
  LI Z L, LONG T, ZHAO B J, et al. Standard modeling and solving methods for large-scale constellation collaborative scheduling for early warning scenarios[J]. Acta Aeronautica et Astronautica Sinica202445(22): 330181 (in Chinese).
[23] UBER TECHNOLOGIES, INC. H3: A hexagonal hierarchical geospatial indexing system[EB/OL]. (2018-01-01) [2025-08-27]. .
[24] MA Y, LI G Q, ZHAO L, et al. Accuracy evaluation method for vector data based on hexagonal discrete global grid[J]. ISPRS International Journal of Geo-Information202514(1): 5.
[25] WAGNER S, STENZEL F, KRUEGER T, et al. Drivers of global irrigation expansion: The role of discrete global grid choice[J]. Hydrology and Earth System Sciences202428(22): 5049-5068.
[26] ZHIBO E, SHI R H, GAN L, et al. Multi-satellites imaging scheduling using individual reconfiguration based integer coding genetic algorithm[J]. Acta Astronautica2021178: 645-657.
[27] XU Y, LIU X, HE R, et al. Multi-satellite scheduling framework and algorithm for very large area observation[C]∥2018 IEEE Congress on Evolutionary Computation (CEC). Piscataway: IEEE Press, 2018: 1-8.
[28] LIU X L, LAPORTE G, CHEN Y W, et al. An adaptive large neighborhood search metaheuristic for agile satellite scheduling with time-dependent transition time[J]. Computers & Operations Research201786: 41-53.
[29] TALBI E G. Metaheuristics: From design to implementation[M]. Hoboken: Wiley, 2009: 513-520.
[30] L?SSIG J, SUDHOLT D. The benefit of migration in parallel evolutionary algorithms[C]∥Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. New York: ACM, 2010: 1105-1112.
Outlines

/