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Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems. Although many scheduling algorithms have been proposed, emergency tasks, characterized as importance and urgency (e.g., observation tasks orienting to the earthquake area and military conflict area), have not been taken into account yet. Therefore, it is crucial to investigate the satellite integrated scheduling methods, which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks. Firstly, a pretreatment approach is proposed, which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites. Secondly, a mathematical model and an acyclic directed graph model are constructed. Thirdly, a hybrid ant colony optimization method mixed with iteration local search (ACO-ILS) is established to solve the problem. Moreover, to guarantee all solutions satisfying the emergency task requirement constraints, a constraint repair method is presented. Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods, the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search, and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments.

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