空间碎片多次观测的多卫星调度方法

doi:10.12305/j.issn.1001-506X.2025.11.17

Abstract

Abstract:

Aiming at the problem of scheduling repeated observations of multiple space targets by multiple satellites within a given time period, a multi-population genetic neighborhood search algorithm (MPGNSA) is proposed. Firstly, taking into account the observation capabilities of satellites and the observation requirements of targets, the design direction is to maximize the benefits of the observation frequency and time of the target in the observation cycle. Then, the constraints of satellite task scheduling and develop a planning model for satellite-based observation of space targets are analyzed. In addition, a parallel evolutionary approach across multi-population is adopted, an encoding scheme and heuristic rules are employed during the evolution process to ensure high-fitness individuals, while a neighborhood search mechanism is introduced to enhance convergence speed. Finally, simulation results show that MPGNSA achieves higher final fitness compared to other algorithms. Under the same computational time constraints, MPGNSA also yields higher task benefits than other algorithms. It can be concluded that MPGNSA has significant advantages in improving task fitness and optimizing scheduling efficiency, offering more efficient solutions within limited computational time.

Key words: satellite mission planning, multi-satellite collaboration, multi-population evolution, genetic algorithm, neighborhood search

CLC Number:

V 11

Dali WANG, Lei DONG, Huawang LI, Zhenzhen ZHENG, Haiying HU. Multi-satellite task scheduling method for repeated observation of space debris[J]. Systems Engineering and Electronics, 2025, 47(11): 3685-3698.

Figures/Tables 28

Table 1

Symbol definition"

符号	定义
$ S $	卫星集合, $ S=\mathrm{ }\{{S}_{1},{S}_{2},{S}_{3}, \cdots , {S}_{{\mathrm{ns}}}\} $，$ {S}_{i} $ 表示第$ i $颗卫星
$ T $	目标集合, $ T=\left\{{T}_{1},{T}_{2},{T}_{3} ,\cdots , {T}_{{\mathrm{nt}}}\right\} $, $ {T}_{j} $代表第$ j $个目标
$ {S}_{{\mathrm{prop}}},{T}_{{\mathrm{prop}}} $	卫星、目标的轨道数据
$ {\mathrm{ns }}$, $ {\mathrm{nt}} $	卫星、目标的数量
$ r $	$ r=\left\{{r}_{1},{r}_{2} ,\cdots, {r}_{{\mathrm{nt}}}\right\} $，$ {r}_{i} $ 为第$ i $个目标所需求的观测次数
$ {\mathrm{tr }}$	$ {\mathrm{tr}}=\left\{{{\mathrm{tr}}}_{1},{{\mathrm{tr}}}_{2} ,\cdots, {{\mathrm{tr}}}_{{\mathrm{nt}}}\right\} $，$ {\mathrm{t}}{{\mathrm{r}}}_{j} $为第$ j $个目标所需每次观测时长
$ {T}_{\mathrm{s}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{c}\mathrm{h}} $	卫星进行任务切换所需要的时间
${\mathrm{ DT }}$	整体规划区间，$ {{\mathrm{DT}}}_{{\mathrm{s}}},{{\mathrm{DT}}}_{{\mathrm{e}}} $为开始与结束时刻
${\mathrm{ TCWST}} $	$ {{\mathrm{TCWS}}}{{\mathrm{T}}}_{ij} $表示卫星$ {S}_{i} $对卫星$ {T}_{j} $的所有可观测时间窗口
${\mathrm{ TAW }}$	每个目标的实际观测时间，$ {{\mathrm{TAW}}}_{jk} $为目标$ {T}_{j} $在区间$ {\mathrm{DT}} $内的第$ k $次实际观测时间； $ {{\mathrm{TAWST}}}_{jk} $为其观测开始时间，$ {{\mathrm{TAWET}}}_{jk} $为其观测结束时间
$ {\mathrm{SAW}} $	$ \mathrm{每}\mathrm{颗}\mathrm{卫}\mathrm{星}\mathrm{实}\mathrm{际}\mathrm{观}\mathrm{测}\mathrm{时}\mathrm{间}\mathrm{序}\mathrm{列}，{{\mathrm{SAW}}}_{ij} $为卫星$ {S}_{i} $在区间${\mathrm{ DT }}$内的第$ j $次任务的观测窗口； $ {{\mathrm{SAWST}}}_{ij} $为其观测开始时间，$ {{\mathrm{SAWET}}}_{ij} $为其观测结束时间
$ {\mathrm{Satload }}$	卫星负载，$ {{\mathrm{Satload}}}_{i} $为卫星$ i $最终工作总时长，含任务执行时长与任务切换时长
$ {X}_{ijk} $	$ {X}_{ijk}为 $布尔变量，记录卫星$ i $在对目标$ j $的第$ k $个观测窗口是否进行了观测（1代表观测，0代表未观测）

Table 1

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卫星编号	a/km	e	i/（°）	ω/（°）	Ω/（°）	M/（°）
Sat1	7378.14	0	99.5	0	10.9	0
Sat2	7378.14	0	99.5	0	10.9	60
Sat3	7378.14	0	99.5	0	10.9	120
Sat4	7378.14	0	99.5	0	10.9	180
Sat5	7378.14	0	99.5	0	10.9	240
Sat6	7378.14	0	99.5	0	10.9	300

编号	a/km	e	i/（°）	ω/（°）	Ω/（°）	M/（°）
Tar1	6899.7	0	143.4	57.09	62.9	6.4
Tar2	7059.02	0	98.13	100.47	90	311.22
Tar3	7484.39	0.04	63.42	358.84	98.61	34.45
Tar4	7097.69	0.01	57.01	130.38	57.49	340.77
Tar5	6736.88	0.01	96.88	191.12	14.87	14.34
						

目标编号	观测次数/次	每次观测时间/s
1	10	240
2	8	300
3	6	480
4	8	480
5	7	300
6	7	300
7	6	420
8	10	420
9	6	420
10	8	420
		

卫星编号	任务数量/个	任务总时长/h	负载率/%
Sat1	82	7.825	65.21
Sat2	89	7.875	65.63
Sat3	89	7.85	65.42
Sat4	87	7.85	65.42
Sat5	83	7.808	65.07
Sat6	88	7.867	65.56
合计	516	—	—

目标编号	观测次数/次	观测时长/s	观测方案
目标编号	观测次数/次	观测时长/s	卫星	开始时刻	结束时刻
目标1	10	240	1	0:39:09	0:43:09
			3	2:06:29	2:10:29
			2	3:06:42	3:10:42
			2	4:41:54	4:45:54
			3	5:21:45	5:25:45
			4	6:51:45	6:55:45
			4	7:51:33	7:55:33
			5	9:26:08	9:30:08
			5	10:02:45	10:06:45
			5	11:43:35	11:47:35
目标2	8	300	1	0:33:39	0:38:39
			2	2:10:28	2:15:28
			1	3:49:47	3:54:47
			2	5:27:28	5:32:28
			2	7:05:46	7:10:46
			3	8:36:12	8:41:12
			4	10:18:57	10:23:57
			4	11:20:02	11:25:02
目标3	6	480	3	0:00:01	0:08:01
			4	1:39:11	1:47:11
			3	3:24:31	3:32:31
			1	5:28:44	5:36:44
			6	8:17:12	8:25:12
			5	10:10:59	10:18:59
目标4	8	480	2	0:03:31	0:11:31
			1	1:56:10	2:04:10
			2	3:35:47	3:43:47
			4	5:06:20	5:14:20
			5	6:38:32	6:46:32
			1	7:15:13	7:23:13
			1	9:01:22	9:09:22
			2	1038:44	10:46:44
目标5	7	300	2	1:34:43	1:39:43
			3	3:18:34	3:23:34
			2	5:01:35	5:06:35
			3	6:32:45	6:37:45
			3	8:14:56	8:19:56
			4	9:51:12	9:56:12
			4	11:38:08	11:43:08

Multi-satellite task scheduling method for repeated observation of space debris

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场景编号	目标数量	任务数量	描述
场景1	30	265	负载较低
场景2	45	384	负载适中
场景3	60	516	负载较高

场景	指标	GA	GNSA	MPGA	MPGNSA
场景1	最大适应度值	0.9091	0.9203	0.9152	0.9250
	平均适应度值	0.9081	0.9190	0.9146	0.9231
	最小适应度值	0.9069	0.9177	0.9177	0.9213
	任务完成数	265	265	265	265
	任务完成率/%	100	100	100	100
场景2	最大适应度值	0.8621	0.8717	0.8721	0.8765
	平均适应度值	0.8596	0.8702	0.8688	0.8756
	最小适应度值	0.8569	0.8688	0.8662	0.8730
	任务完成数	384	384	384	384
	任务完成率/%	100	100	100	100
场景3	最大适应度值	0.7321	0.7416	0.7462	0.7475
	平均适应度值	0.7270	0.7357	0.7429	0.7470
	最小适应度值	0.7217	0.7316	0.7401	0.7458
	任务完成数	516	516	516	516
	任务完成率/%	100	100	100	100

场景	$ \mathrm{G}\mathrm{A} $	$ \mathrm{G}\mathrm{N}\mathrm{S}\mathrm{A} $	$ \mathrm{M}\mathrm{P}\mathrm{G}\mathrm{A} $	$ \mathrm{M}\mathrm{P}\mathrm{G}\mathrm{N}\mathrm{S}\mathrm{A} $
场景1	27.1	32.4	47.4	51.7
场景2	43.5	52.3	69.3	76.3
场景3	63.7	71.6	98.6	107.3

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卫星编号	任务数量/个	任务总时长/h	负载率/%
Sat1	77	7.333	61.11
Sat2	83	7.383	61.53
Sat3	82	7.358	61.32
Sat4	82	7.358	61.32
Sat5	80	7.317	60.97
Sat6	79	7.367	61.39
合计	483	—	—