Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm

Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First,...

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Vydáno v:Computer modeling in engineering & sciences Ročník 122; číslo 1; s. 235 - 257
Hlavní autoři: Zhao, Yanbin, Du, Bin, Li, Shuang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 01.01.2020
Témata:
Agile Satellite
Algorithms
Clustering
Computer simulation
Economic models
Mission Planning
Satellite observation
Schedules
Tabu Algorithm
Task Clustering
ISSN:1526-1492, 1526-1506, 1526-1506
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Abstract Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a preprocessing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path is obtained by adopting Tabu algorithm in the outer layer. Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.
AbstractList Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a pre-processing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path is obtained by adopting Tabu algorithm in the outer layer. Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.
Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a preprocessing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path is obtained by adopting Tabu algorithm in the outer layer. Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.
Author Zhao, Yanbin
Du, Bin
Li, Shuang
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  surname: Li
  fullname: Li, Shuang
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SubjectTerms Agile Satellite
Algorithms
Clustering
Computer simulation
Economic models
Mission Planning
Satellite observation
Schedules
Tabu Algorithm
Task Clustering
Title Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm
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