Sky Area Target of Opportunity Mission Planning Method
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摘要: 针对中法合作SVOM卫星的天区范围内机遇目标规划问题（ToO-MM），对其中的约束条件和优化目标进行抽象，建立了规划问题数学模型，设计实现了基于启发式规则的机遇目标规划算法TMHPA。以最大化卫星科学观测收益和最大化应急任务响应度为优化目标，考虑卫星姿态调整时间的影响，对观测任务和数传任务进行规划。通过仿真实验验证算法的有效性，结果表明该方法能够在保证算法收敛性和时效性同时，给出卫星在天区范围内的网格单元（tile）目标观测序列以及执行数传任务的时段安排，实现对ToO目标观测的快速响应，并及时下传机遇目标科学观测数据，满足规划算法的设计需求。Abstract: In regard to the planning problem of ToO-MM (a type of sky area Target of Opportunity) for SVOM (Space-based multi-band astronomical Variable Objects Monitor) mission, the constraints and optimization goals are abstracted, the mathematical description model is established, and the algorithm TMHPA (ToO-MM Heuristic Planning Algorithm) based on heuristic strategy is designed. The algorithm aims at maximizing satellite scientific observation benefits and maximizing emergency task response, and considering the influence of satellite attitude adjustment time, the observation tasks and data transmission tasks can be planned. An example is given to verify the effectiveness of this algorithm. The results show that the method executed in this paper is not only able to solve the problem of ToO-MM, but also to ensure the convergence and timeliness. TMHPA can give the target observation sequence of the satellite within the sky area and the time period arrangement for performing data transmission tasks. Compared with GA (Genetic Algorithm) and DEA (Differential Evolution Algorithm), it has a shorter computational time overhead and a smaller attitude adjustment time. The TMHPA algorithm could achieve the rapid response to the target observation, and meet the design requirements as well.
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表 1 ToO-MM规划模型基本符号定义
Table 1. Parameter definition of ToO-MM model
符号 描述 T 机遇目标tile集合 W 目标可视时间窗口 G 地面数传站可视窗口 R 观测任务规划结果 K 数传任务规划结果 N tile的总数量 S 姿态调整时长 E 姿态调整时段 C 一个圈次观测tile数量 O 存储量 
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表 2 GW170814天区机遇目标tile数据
Table 2. Tile data of GW170814
编号 赤经/（º） 赤纬/（º） 观测时长/min 观测优先级 1 47.857142 –42.609806 10 1.589328 2 45.775862 –46.571847 10 1.553358 3 46.428571 –42.609806 10 1.425764 4 47.950812 –44.201529 10 1.415292 ··· ··· ··· ··· ··· 228 35.816326 –53.572233 10 0.068670 229 39.919354 –43.406858 10 0.066731 230 50.901639 –44.201529 10 0.066025
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表 3 TMPHA规划结果
Table 3. Planning result of TMPHA
年-月-日 时刻 (UT) 积秒 编号 圈次 持续时长/s 调姿时间/s 2022-09-26 01:24:56 23160296 4 3975 733 108 2022-09-26 02:15:25 23163325 5 3976 600 104 2022-09-26 02:27:09 23164029 7 3976 600 105 2022-09-26 02:38:54 23164734 6 3976 600 108 ··· ··· ··· ··· ··· ··· ··· 2022-09-26 23:39:30 23240370 56 3989 600 113 2022-09-26 23:51:23 23241083 57 3989 600 105 2022-09-27 00:03:08 23241788 60 3989 958 105
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表 4 各个优化目标函数值
Table 4. Value of each optimization objective function
优化目标 $ {f}_{\mathrm{p}\mathrm{r}\mathrm{o}} $ $ {f}_{\mathrm{s}\mathrm{l}\mathrm{e}\mathrm{w}} $ $ {f}_{\mathrm{p}\mathrm{r}\mathrm{i}} $ F 数值 0.0124 0.028 0.043 0.0641
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表 5 算法对比结果
Table 5. Comparison results of algorithm
算法 GA DEA TMHPA 总观测收益 54.17 52.32 50.90 卫星调姿用时/s 8502.11 8471.83 8199.36 算法总耗时/s 144.31 135.45 3.78
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