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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Veröffentlicht in:Computer modeling in engineering & sciences Jg. 122; H. 1; S. 235 - 257
Hauptverfasser: Zhao, Yanbin, Du, Bin, Li, Shuang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Henderson Tech Science Press 01.01.2020
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:1526-1492(20200115)122:1L.235;1-
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1526-1492
1526-1506
1526-1506
DOI:10.32604/cmes.2020.08070