During the observations made by imaging satellites, meteorological factors are likely to change frequently. The vagaries of weather conditions and significant effects on the actual observation results mean that there is an urgent need to apply more intelligence to satellite mission
planning. Thus, this paper describes an autonomous replanning method for imaging satellites that considers the real-time weather conditions. Considering the characteristics of different input data, this method replans the low-yield task set and fine-tunes others to improve profitability. Moreover,
the proposed method can heuristically select the appropriate adjustment rule to achieve autonomous satellite mission planning. A series of simulations with various task quantities and in different environments shows that the proposed method can respond effectively to real-time weather changes,
and can steadily improve the total profits in a variety of weather conditions during Earth observation activities.
Agile Satellite Mission Planning Via Task Clustering and Double-Layer Tabu Algorithm
