面向点群与大区域目标的成像卫星任务规划模型与算法研究

doi:10.12305/j.issn.1001-506X.2025.09.15

Abstract

Abstract:

Aiming at the imaging requirements of new complex targets such as point-cluster and large-region targets, to improve utilization efficiency of satellite resources and complpete income of observation tasks, the imaging satellite mission planning model and algorithm for point-cluster and large-region targets are studied. Firstly, a unified decision model is constructed, and the stripe elongation rate is introduced as a decision variable to solve the problem of imaging representation of point-cluster targets. Constraints are considered including execution timing, power, sequestration, and conversion time. The task benefit model based on stripe elongation rate and grid coverage rate is established separately according to the characteristics of targets. Then, an adaptive wave-temperature control simulated annealing algorithm combined with tabu strategy is designed to provide adaptive and efficient intelligent model solving methods, which improves the global optimization ability through the wave-temperature control and internal-cycle-number update strategies, and enhances the local search ability by tabu strategy. Simulation experiments show that the model and algorithm can effectively improve the planning effect. The proposed algorithm and model are suitable for imaging satellite mission planning problems for point-cluster and large-region targets.

Key words: satellite mission planning, point-cluster target, large-region target, wave-temperature control, simulated annealing

CLC Number:

V 19
TP 18

Jianbo YUAN, Yonghao DU, Yingguo CHEN, Yongming HE. Research on imaging satellite mission planning model and algorithm for point-cluster and large-region targets[J]. Systems Engineering and Electronics, 2025, 47(9): 2939-2950.

Figures/Tables 22

Table 1

Symbol description"

符号	释义
T	任务集，T = $T^{\mathrm{P}} $∪$T^{\mathrm{A}} $ = {t_i \| i=1,2,3···}
$T^{\mathrm{P}} $, $T^{\mathrm{A}} $	点群目标任务集、大区域目标任务集
A	大区域目标集合，A = {a_i \| i=1,2,3···}
S	卫星集合，S = {s_j \| j = 1,2,3···}
H	数传站集合，H = {h_k \| k = 1,2,3···}
G	离散化的网格点集合，G = {g_m \| m = 1,2,3···}
c_i	第i个大区域目标的总面积
$ g_m^{a_i} $	第m个网格点包含大区域目标a_i的面积
X_i	任务t_i的成像机会集合
Y_i	任务t_i的数传机会集合
x_ij	0-1决策变量，若任务t_i的成像动作在其第j个成像机会内执行，则 x_ij = 1；否则x_ij = 0
y_ij	0-1决策变量，若任务t_i的数传动作在其第j个数传机会内执行，则 y_ij = 1；否则y_ij = 0
z_i	决策变量，表示任务t_i的成像条带在其成像方向上的延长比例，简称条带延长率
f_i	任务t_i的收益
m_i	任务t_i成像动作所产生的数据量
q_i	任务t_i成像动作耗电量
$ b_i^{\mathrm{O}},e_i^{\mathrm{O}} $	任务t_i成像动作的开始时间、结束时间
$b^\mathrm{D}_ {i},e^\mathrm{D}_{i} $	任务t_i数传动作的开始时间、结束时间
s_o（t_i）	执行任务t_i成像动作的卫星
s_d（t_i）	执行任务t_i数传动作的卫星
s（t_i）	执行任务t_i的卫星
h（t_i）	执行任务t_i的数传站
o（t_i）	执行任务t_i所在的卫星轨道
O_j	卫星s_j的轨道集合
o_jk	卫星s_j第k次过境形成的轨道
M_j	卫星s_j的固存容量上限
Q_j	卫星s_j的电量上限
∆K（$t_i $, $t_{i' }$）	任务$t_i $和$t_{i' } $的成像动作间转换时间最小值
∆N	卫星执行成像动作与数传动作间转换时间最小值
∆D	同一卫星对不同数传站执行数传动作转换时间最小值
∆P	同一数传站接收不同卫星数传动作转换时间最小值
T_j	卫星s_j执行的任务序列
w（t_i, time）	0-1变量，若time时刻下任务t_i已完成成像动作，则w(t_i, time) = 1，否则w(t_i, time) = 0
d（t_i, time）	0-1变量，若time时刻下任务t_i已完成数传动作，则d(t_i, time) = 1，否则d(t_i, time) = 0
v_m	0-1变量，若网格g_m被条带所覆盖，则v_m = 1；否则v_m = 0
φ_i	第i个大区域目标的覆盖率

Table 1

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Table 6

Fig.7

Table 7

Fig.8

Table 8

Fig.9

Fig.10

Table 9

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Table 11

Fig.11

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场景	卫星	成像窗口	成像动作执行机会	数传站	数传窗口	数传动作执行机会
点群目标	24	1 902	117 569	23	2 003	505 006
大区域目标	13	2 306	2 306	23	2 784	689 553

指标	TS	SA	LA	WCSA	AWCSAT
收益	351	219	362.7	381	383.1
标准差	13.30	24.35	14.22	7.13	6.33

T₀	收益	标准差
E（X_avg）	369.3	12.28
∆E/ln 0.8	380.9	6.57
∆E/ln 0.9	383.1	6.33
∆E/ln 0.95	366.8	10.96

L₀	收益	标准差
100	377.1	14.41
200	383.1	6.33
300	377.1	7.26
500	363.7	7.69

K	收益	标准差
1000	383.9	8.03
2000	384.9	6.79
3000	383.1	6.33
5000	383.2	6.49

Research on imaging satellite mission planning model and algorithm for point-cluster and large-region targets

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Figures/Tables 22

References 30

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目标处理方式	不合并不延长	合并不延长	合并且延长
收益	44.6	303.8	383.1
任务完成率/%	11.15	75.95	95.775

算法	收益	标准差
TS	41079.4	490.63
SA	41168.6	892.06
LA	42462.1	669.045
WCSA	42906.5	577.91
AWCSAT	42956.1	191.96

K	收益	标准差
1 000	42 845.2	154.55
3 000	42 750	480.10
5 000	42 956.1	191.96
7 000	41 664.2	717.28

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