Abstract
Satellite task planning not only plans the observation tasks to collect images of the earth surface, but also schedules the transmission tasks to download images to the ground station for users’ using, which plays an important role in improving the efficiency of the satellite observation system. However, most of the work to our knowledge, scheduling the observation and transmission tasks separately, ignores the correlation between them in resource (e.g., energy and memory) consumption and acquisition. In this paper, we study the single-satellite observation and transmission task planning problem under a more accurate resource usage model. Two preprocessing strategies including graph partition and nondominated subpaths selection are used to decompose the problem, and an improved label-setting algorithm with the lower bound cutting strategy is proposed to maximize the total benefit. Finally, we compare the proposed method with other three algorithms based on three data sets, and the experimental result shows that our method can find the near-optimal solution in much less time.
Highlights
Earth observation satellite (EOS) collects images of the earth surface and downloads them to the ground station for users’ using, which has been widely used in environmental monitoring, resource management, agricultural analysis, military reconnaissance, and another aspect of life [1,2,3]
The observation payload takes images and stores them in the storage when a target is in the view of the EOS; this process is called “observation.” The transmission payload downloads images to the ground and frees the corresponding storage when the EOS is in the visible range of a ground station; this process is called “transmission.” Energy is consumed in the process of observation and transmission and acquired when the EOS is in the sun
The results demonstrate that our method can find the nearoptimal solution in much less time and indicate the ability of our method to solve the satellite observation and transmission tasks planning problem (SOTTP) under different task size
Summary
Earth observation satellite (EOS) collects images of the earth surface and downloads them to the ground station for users’ using, which has been widely used in environmental monitoring, resource management, agricultural analysis, military reconnaissance, and another aspect of life [1,2,3]. The main contribution includes the following: (1) we develop a directed acyclic graph model and propose two preprocessing strategies including graph partition and nondominated subpaths selection. These two preprocessing strategies decompose the graph model into a series of subgraphs that effectively reduce the complexity of the problem. (2) We construct an equivalent mathematical model and propose an improved label-setting algorithm with the lower bound cutting strategy to solve the SOTTP. This strategy removes the low-benefit paths in each subgraph that improves the search efficiency.
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