基于网格剖分的敏捷卫星区域目标观测调度方法

doi:10.7527/S1000-6893.2025.32713

Abstract

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.

Key words: area target observation, agile satellite, multiple-strip observation, hexagonal grid, bilevel optimization framework

CLC Number:

Guohua WU, Xinyang ZHOU, Yi GU, Wei TANG. Grid subdivision-based agile satellite scheduling method for observing area targets[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(14): 332713.

Figures/Tables 23

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Table 1

Table 2

Table 3

Satellite orbit parameter value

参数	取值
轨道半长轴/km	6 878.137
偏心率	0
倾角/（°）	（30，50）
近地点幅角/（°）	（0，180）
升交点赤经/（°）	（0，360）
平近点角/（°）	（45，360）
载荷半角/（°）	1.6942
俯仰角/（°）	（-20，20）
滚转角/（°）	（-30，30）
$E i$ /（kJ）	30 000
$e i$ /（J·s^-1）	150

Table 3

Table 4

Main parameters of SACPG-DO

参数	取值
初始温度 $T$	100
冷却率 $α t$	0.99
最大迭代次数	200
历史得分权重系数 $ε$	0.1
覆盖增益权重 $λ$	1.8
衰减因子 $χ$	0.8
禁忌表长度	10
下层最大迭代次数	100
粒子数量	40
种群数量	40
种群交流周期 $T e x$	20
连续无显著改进轮数 $L$	40
权重因子 $κ$	0.7

Table 4

Fig.11

Table 5

Comparison of algorithm average coverage rate （N=2）

场景	性能指标	算法
场景	性能指标	SACPG-DO	SAPSO-DO	SAGA-DO	SAG-DO	IGCPG-DO	IGPSO-DO	IGGA-DO	CPG	PSO	GA	ACO	SA
C1	覆盖率	0.840 0	0.800 9	0.787 0	0.736 5	0.682 1	0.642 5	0.584 3	0.493 6	0.472 3	0.493 5	0.492 8	0.487 4
C1	耗时/s	94.631 1	49.523 6	69.119 4	95.749 6	169.687 6	190.965 7	181.204 9	3.661 6	4.832 5	0.788 8	15.097 8	0.131 3
C2	覆盖率	0.915 8	0.885 4	0.863 2	0.843 5	0.704 9	0.664 9	0.647 9	0.557 8	0.534 2	0.554 9	0.553 9	0.554 0
C2	耗时/s	109.258 7	53.083 4	76.886 4	109.667 1	191.228 5	216.780 3	186.078 2	4.465 4	5.556 2	0.925 5	25.657 2	0.163 0
C3	覆盖率	0.870 3	0.848 0	0.834 9	0.800 4	0.802 1	0.756 2	0.753 7	0.727 6	0.684 6	0.718 8	0.678 6	0.688 9
C3	耗时/s	108.062 9	48.274 0	71.836 8	89.651 9	338.712 9	312.059 4	217.606 7	4.962 6	5.594 2	1.014 1	28.010 7	0.182 6
C4	覆盖率	0.998 5	0.975 6	0.960 3	0.953 0	0.912 5	0.877 9	0.890 4	0.868 6	0.817 7	0.859 2	0.806 2	0.829 7
C4	耗时/s	135.764 2	54.738 1	85.967 8	111.718 4	338.712 9	312.059 4	217.606 7	6.693 0	7.395 6	1.494 5	57.986 4	0.284 9
C5	覆盖率	0.626 6	0.558 0	0.576 4	0.522 2	0.457 0	0.430 3	0.376 9	0.334 3	0.323 6	0.332 7	0.334 3	0.308 3
C5	耗时/s	98.833 3	89.426 6	138.396 8	107.163 6	272.948 5	614.658 7	658.959 5	3.881 1	5.201 2	0.878 4	15.740 9	0.138 2
C6	覆盖率	0.819 7	0.766 7	0.724 6	0.710 8	0.624 5	0.593 6	0.549 3	0.513 1	0.489 8	0.510 9	0.513 1	0.495 4
C6	耗时/s	94.167 5	50.409 2	68.312 7	94.208 9	168.532 6	190.443 9	178.726 4	3.645 6	4.425 3	0.770 7	11.430 2	0.135 5
C7	覆盖率	0.797 7	0.761 8	0.752 5	0.719 4	0.588 7	0.562 1	0.526 9	0.440 0	0.421 0	0.439 1	0.440 0	0.432 8
C7	耗时/s	82.339 5	48.531 6	62.185 7	85.089 4	154.779 9	171.866 9	169.4154	3.039 1	4.033 6	0.666 5	9.218 6	0.118 0
C8	覆盖率	0.811 6	0.786 4	0.787 8	0.761 2	0.600 0	0.571 0	0.531 7	0.487 9	0.467 5	0.482 0	0.481 8	0.466 3
C8	耗时/s	119.526 5	66.201 7	88.177 7	125.892 2	460.377 1	402.760 1	835.096 5	5.648 7	6.136 6	1.332 8	27.192 6	0.198 3
C9	覆盖率	0.941 9	0.900 5	0.899 1	0.862 6	0.763 1	0.734 7	0.728 4	0.678 8	0.654 3	0.669 9	0.653 4	0.665 9
C9	耗时/s	176.982 3	85.181 2	124.107 1	181.562 7	342.526 0	251.042 4	952.930 2	9.215 4	10.169 6	2.170 6	91.424 8	0.352 5
C10	覆盖率	0.808 9	0.731 8	0.746 0	0.690 0	0.534 6	0.523 2	0.380 2	0.380 2	0.363 1	0.376 2	0.374 4	0.349 7
C10	耗时/s	282.986 3	79.508 6	108.006 9	171.306 6	212.188 8	253.129 7	6.315 6	6.315 6	8.120 7	1.245 1	34.509 5	0.252 0
C11	覆盖率	0.863 9	0.774 5	0.782 3	0.730 8	0.594 5	0.572 2	0.550 3	0.516 4	0.491 0	0.509 9	0.505 8	0.497 7
C11	耗时/s	402.767 7	83.405 3	119.252 7	179.189 3	363.163 6	230.446 0	265.808 0	8.257 8	10.135 2	1.594 8	56.215 0	0.358 3
C12	覆盖率	0.969 9	0.893 5	0.898 7	0.875 4	0.661 0	0.624 5	0.628 8	0.627 5	0.592 4	0.628 0	0.612 6	0.593 1
C12	耗时/s	414.780 6	95.263 7	147.107 0	284.925 2	277.076 2	340.357 0	294.984 3	10.416 9	13.327 4	2.174 9	88.250 7	0.453 9
C13	覆盖率	0.714 5	0.671 0	0.676 8	0.661 7	0.440 2	0.419 0	0.430 7	0.439 0	0.416 4	0.436 1	0.420 4	0.422 8
C13	耗时/s	675.038 7	221.388 1	172.209 0	418.650 2	1 169.808 5	1 293.959 3	1 325.862 0	10.156 1	11.437 8	2.548 3	105.063 7	0.458 0
C14	覆盖率	0.828 9	0.766 5	0.754 4	0.752 4	0.571 6	0.545 4	0.573 3	0.535 4	0.506 6	0.533 2	0.510 1	0.519 2
C14	耗时/s	264.672 0	160.650 6	199.940 2	317.924 2	436.331 3	519.649 3	467.543 2	13.127 8	14.947 7	3.405 1	173.029 5	0.674 9

Table 5

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Fig.14

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区域编号	区域经纬度/（°）	区域面积/km²	生成网格数量	计算网格数量
Area 1	［10.8，-76.2；11.3，-74.0；12.5，-74.7；12.9，-76.3；12.1，-77.2；11.2，-76.9］	52 965.89	257	1 653
Area 2	［25.0，109.7；24.2，113.9；23.6，112.8；23.3，110.8；23.7，109.1］	54 351.81	254	1 604
Area 3	［-21.0，47.0；-21.3，49.2；-22.5，50.5；-23.2，49.1；-22.7，47.2］	56 699.26	260	1 682
Area 4	［34.8，120.5；33.7，122.5；32.0，124.1；31.4，122.7；32.3，120.9；33.3，120.3］	67 326.91	406	2 650
Area 5	［21.5，10；20，10；20，15；21.5，15］	86 467.34	350	2 241
Area 6	［15.4，-11.8；15.1，-14.5；13.9，-15.7；12.0，-15.0；11.6，-13.0；12.7，-10.8；14.2，-10.6］	170 621.85	786	5 278

场景编号	区域编号	卫星数量
C1	Area 1	2
C2	Area 1	3
C3	Area 2	3
C4	Area 2	4
C5	Area 3	2
C6	Area 3	3
C7	Area 4	1
C8	Area 4	2
C9	Area 4	3
C10	Area 5	4
C11	Area 5	5
C12	Area 5	6
C13	Area 6	3
C14	Area 6	4

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

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[1]	Hai LI, Yongjun LI, Yuanhao LIU, Weihu ZHAO, Xin LI, Shanghong ZHAO. ESWO⁃based task⁃scheduling algorithm for agile earth observation satellites [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(10): 329370-329370.
[2]	ZHAO Lin, WANG Shuo, HAO Yong, LIU Yuan, CHAI Yi. Mission planning for agile satellite based on the mapping relationship between ground missions and spatial attitudes [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(10): 322066-322066.