面向巨星座态势感知的多约束任务规划

doi:10.12305/j.issn.1001-506X.2025.09.26

摘要/Abstract

摘要：

巨星座给空间安全带来隐患，因此对巨星座卫星的状态进行态势感知尤为重要。采用小规模Walker-δ型感知星座，通过与巨星座卫星近距离掠飞实现侦察。在考虑姿态机动时间、载荷充放电等多约束情况下，提出一种基于滚动规划框架的分层式动态任务规划方法。初规划层基于并行规划原则和二进制鲸鱼优化算法（binary whale optimization algorithm，BWOA）对多星感知多目标问题进行规划；重归划层对初规划中无法被规划的任务采用贪婪动态调整策略；同时通过多滚动周期规划提高算法寻优质量。仿真结果表明，规划后感知星座能在12 h内完成对所研究的巨星座卫星的全面侦察，验证了所提方法的有效性。

关键词: 巨星座, 态势感知, 分层任务规划, 二进制鲸鱼优化算法

Abstract:

The development of mega-constellation brings hidden danger to the space security, so it is particularly important to conduct situational awareness of the status of mega-constellation satellites. A hierarchical dynamic mission planning method based on rolling planning framework is proposed by using a small-scale Walker-δ sensing constellation to achieve effective reconnaissance through a close flyby with mega-constellation satellites, considering the constraints of attitude maneuver time and load charge and discharge. The initial planning layer is based on the parallel planning principle and binary whale optimization algorithm （BWOA） to plan the multi-satellite sensing multi-objective problem. The replanning layer adopts the greedy dynamic adjustment strategy to assign the tasks that cannot be assigned in the initial planning results. At the same time, the optimization quality of the algorithm is improved by multi-rolling cycle planning. Simulation results show that the sensing constellation can complete the comprehensive and reconnaissance of mega-constellation satellites studied within 12 h after planning, which verifies the effectiveness of the proposed method.

Key words: mega-constellation, situational awareness, hierarchical task planning, binary whale optimization algorithm （BWOA）

中图分类号:

V 19

蒋怡, 毛雨荷, 岳程斐, 吴云华. 面向巨星座态势感知的多约束任务规划[J]. 系统工程与电子技术, 2025, 47(9): 3047-3057.

Yi JIANG, Yuhe MAO, Chengfei YUE, Yunhua WU. Mega-constellation situational awareness mission planning with multi-constraints[J]. Systems Engineering and Electronics, 2025, 47(9): 3047-3057.

图/表 18

图1

图2

图3

图4

图5

图6

图7

图8

表1

表2

图9

表3

图10

图11

图12

表4

表5

图13

参考文献 35

1	CHEN Q, GIAMBENE G, YANG L, et al. Analysis of inter-satellite link paths for LEO mega-constellation networks[J]. IEEE Trans. on Vehicular Technology, 2021, 70 (3): 2743- 2755. doi: 10.1109/TVT.2021.3058126
2	SI Y T, ZHANG E T, ZHANG W, et al. A survey on the development of low-orbit mega-constellation and its TT&C methods[C]//Proc. of the 5th International Conference on Information Communication and Signal Processing, 2022: 324−332.
3	REN S Y, YANG X H, WANG R L, et al. The interaction between the LEO satellite constellation and the space debris environment[J]. Applied Sciences, 2021, 11 (20): 9490. doi: 10.3390/app11209490
4	ZHANG Y, LI B, LIU H K, et al. An analysis of close approaches and probability of collisions between LEO resident space objects and mega constellations[J]. Geo-spatial Information Science, 2022, 25 (1): 104- 120. doi: 10.1080/10095020.2022.2031313
5	BOLEY A C, BUERS M. Satellite mega-constellations create risks in low Earth orbit, the atmosphere and on Earth[J]. Scientific Reports, 2021, 11 (1): 10624. doi: 10.1038/s41598-021-90191-w
6	MOHAN N, FERGUSON A E, CECH H, et al. A multifaceted look at Starlink performance[C]//Proc. of the ACM on Web Conference, 2024: 2723−2734.
7	CORRADO R, BERTHET M, SAKAL M. Starlink for ASEAN: a changemaker in the race toward sustainable development?[J]. Space Policy, 2023, 65, 101554. doi: 10.1016/j.spacepol.2023.101554
8	BOJOR L, PETRACHE T, CRISTESCU C. Emerging technologies in conflict: the impact of Starlink in the Russia–Ukraine war[J]. Land Forces Academy Review, 2024, 29 (2): 185- 194. doi: 10.2478/raft-2024-0020
9	WOOTTON S. Enabling GEODSS for space situational awareness (SSA)[C]//Proc. of the Advanced Maui Optical and Space Surveillance Technologies Conference, 2016.
10	GENG J, LYU N, WANG Z W, et al. Technological development and application of GSO agile small satellite[C]//Proc. of the 6th International Conference on Signal and Information Processing, Networking and Computers, 2020: 718−725.
11	MCCALL G H, DARRAH J H. Space situational awareness: difficult, expensive and necessary[J]. Air & Space Power Journal, 2014, 28 (6): 6- 16.
12	HUA B, YANG G, WU Y H, et al. Angle-only target tracking method for optical imaging micro-/nanosatellite based on APSO-SSUKF[J]. Space: Science & Technology, 2022, 170: 289−301.
13	张晟宇, 孙煜坤, 朱振才, 等. 启发式前后向链条优化组合在轨多目标观测规划算法[J]. 系统工程与电子技术, 2021, 43 (5): 1262- 1269. doi: 10.12305/j.issn.1001-506X.2021.05.13
	ZHANG S Y, SUN Y K, ZHU Z C, et al. Heuristic optimized forward-backward chains combination method for on-board multi-targets observation planning[J]. Systems Engineering and Electronics, 2021, 43 (5): 1262- 1269. doi: 10.12305/j.issn.1001-506X.2021.05.13
14	佘玉成, 杨志, 王晓宇, 等. 基于双向锁定规则的多星任务规划算法[J]. 南京航空航天大学学报, 2022, 54 (S1): 43- 47.
	SHE Y C, YANG Z, WANG X Y, et al. A mission planning algorithm for multi-satellites based on bidirectional locking rules[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2022, 54 (S1): 43- 47.
15	ZHANG S Y, ZHU Z C, HU H Y. Burst tasks scheduling method for infrared LEO constellation based on multi-strategies[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 48 (12): 2405- 2414.
16	WU Y H, YU Z C, LI C Y, et al. Reinforcement learning in dual-arm trajectory planning for a free-floating space robot[J]. Aerospace Science and Technology, 2020, 98, 105657. doi: 10.1016/j.ast.2019.105657
17	ZHANG T, ZHU Y J, MA D Y, et al. Toward rapid and optimal strategy for swarm conflict: a computational game approach[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 3108- 3120. doi: 10.1109/TAES.2024.3361436
18	YANG Y, LIU D S. A hybrid discrete artificial bee colony algorithm for imaging satellite mission planning[J]. IEEE Access, 2023, 11, 40006- 40017. doi: 10.1109/ACCESS.2023.3269066
19	HUANG W Q, WANG H, YI D B, et al. A multiple agile satellite staring observation mission planning method for dense regions[J]. Remote Sensing, 2023, 15 (22): 5317. doi: 10.3390/rs15225317
20	LU Z Z, SHEN X, LI D R, et al. Multiple super-agile satellite collaborative mission planning for area target imaging[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 117, 103211. doi: 10.1016/j.jag.2023.103211
21	BIANCHESSI N, RIGHINI G. Planning and scheduling algorithms for the COSMO-SkyMed constellation[J]. Aerospace Science and Technology, 2008, 12 (7): 535- 544. doi: 10.1016/j.ast.2008.01.001
22	HE L, LIU X L, LAPORTE G, et al. An improved adaptive large neighborhood search algorithm for multiple agile satellites scheduling[J]. Computers and Operations Research, 2018, 100, 12- 25. doi: 10.1016/j.cor.2018.06.020
23	YIN X D, BAI M, LI Z H. Clustering-scheduling methods for oversubscribed short-term tasks of astronomical satellites[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2023, 40 (3): 307- 322.
24	BAREA A, URRUTXUA H, CADARSO L. Large-scale object selection and trajectory planning for multi-target space debris removal missions[J]. Acta Astronautica, 2020, 170, 289- 301. doi: 10.1016/j.actaastro.2020.01.032
25	GUO J, PANG Z J, DU Z H. Optimal planning for a multi-debris active removal mission with a partial debris capture strategy[J]. Chinese Journal of Aeronautics, 2023, 36 (6): 256- 265. doi: 10.1016/j.cja.2023.03.013
26	YUE C F, HUO T, LU M, et al. A systematic method for constrained attitude control under input saturation[J]. IEEE Trans. on Aerospace and Electronic Systems, 2023, 59 (5): 6005- 6015.
27	ZHANG Y, WANG X Y, XI K W, et al. Comparison of shadow models and their impact on precise orbit determination of BeiDou satellites during eclipsing phases[J]. Earth Planets Space, 2022, 74 (1): 126. doi: 10.1186/s40623-022-01684-5
28	LI H J, LIU Y H, LI K B, et al. Analytical prescribed performance guidance with field-of-view and impact-angle constraints[J]. Journal of Guidance, Control, and Dynamics, 2024, 47 (4): 728- 742.
29	XIE H Y, WU B L, BERNELLI-ZAZZERA F. High minimum inter-execution time sigmoid event-triggered control for spacecraft attitude tracking with actuator saturation[J]. IEEE Trans. on Automation Science and Engineering, 2022, 20 (2): 1349- 1363.
30	HAN P, HE Z W, GENG Y Z, et al. Mission planning for agile earth observing satellite based on genetic algorithm[C]//Proc. of the Chinese Control Conference, 2019: 2118−2123.
31	何磊. 敏捷卫星协同调度模型与算法[D]. 长沙: 国防科技大学, 2019.
	HE L. Agile earth observation satellites coordinating and scheduling: models and algorithms[D]. Changsha: National University of Defense Technology, 2019.
32	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95, 51- 67. doi: 10.1016/j.advengsoft.2016.01.008
33	NADIMI-SHAHRAKI M H, ZAMANI H, ASGHARI V Z, et al. A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations[J]. Archives of Computational Methods in Engineering, 2023, 30 (7): 4113- 4159. doi: 10.1007/s11831-023-09928-7
34	REDDY K S, PANWAR L, PANIGRAPHI B K, et al. Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets[J]. Engineering Optimization, 2019, 51 (3): 369- 389. doi: 10.1080/0305215X.2018.1463527
35	CAKAJ S. The parameters comparison of the “Starlink” LEO satellites constellation for different orbital shells[J]. Frontiers in Communications and Networks, 2021, 2, 643095. doi: 10.3389/frcmn.2021.643095

参数	量值
${d_{\min }}$/s	10
$e_i^j$/E	0.1
${E_{\min }}$/E	0.2
${t_{{\mathrm{sun}}}}$/h	1
${t_d}$/min	2

参数	量值
轨道高度/km	490
星座卫星总数	99
轨道倾角/（°）	43
轨道面数	33
每轨道面卫星颗数	3
轨道偏心率	0
相位因子	1

算法	完成任务数	对巨星座覆盖率/%	算法运行时间/s
HRP-BWOA	1397	100	104.823 3
传统方法	798	57	16.528 4
HRP-GA	1397	100	145.281 5
统一优化方法	1075	76	44.125 4

规划类型	任务编号	可见时间窗口/s	任务执行前剩余电量/E	是否执行
初规划	M959	[855,921]	1.0	1
初规划	M276	[1465,1473]	0.9	0
初规划	M264	[1586,1600]	0.9	1
初规划	M1360	[1788,1802]	0.8	1
初规划	M323	[1844,1851]	0.7	0
初规划	M395	[1979,1992]	0.7	1
初规划	M183	[2474,2552]	0.6	1
初规划	M1	[3403,3428]	0.5	1
初规划	M749	[3673,3693]	0.4	1
初规划	M774	[3865,3918]	0.3	1
初规划	M1307	[4245,4254]	0.2	0
初规划	M525	[5824,5847]	0.2	0
初规划	M779	[6387,6451]	0.2	0
初规划	M1255	[6840,6852]	0.2	0
初规划	M1086	[8364,8384]	1.0	1
初规划	M76	[9340,9355]	0.9	1

任务编号	感知卫星	执行时间窗口/s	所在滚动周期
M276	s58	[8808,8834]	2
M323	s52	[7920,7962]	2
M1307	s26	[21933,21954]	4
M1290	s26	[13551,13570]	3
M525	s3	[22953,22984]	4
M779	s32	[9532,9544]	2
M1255	s61	[14056,14077]	3