中文摘要：

针对采用天气预报的滞后云层进行卫星调度影响观测图像质量和观测收益的问题,提出一种获取实时云层的数学模型,并基于此构建考虑实时变换云层的敏捷观测卫星（AEOS）调度模型。由于贪婪搜索算法（GSA）具有局部优化的特性,能够充分考虑卫星观测的云层和有限存储资源等约束,研究了GSA在该卫星调度问题中的应用。首先,GSA优先考虑观测任务的云层遮挡,并根据云层遮挡大小,计算待观测任务的图像质量,将之排序选择待观测的任务;其次,结合任务的大小、截止时间和卫星的存储资源约束,选择能够给观测收益带来最大化的任务;最后,进行观测和任务传送。仿真实验表明,在任务数为100的情况下,采用GSA进行卫星调度的任务收益比常用于卫星调度的动态规划算法（DPA）所获得任务收益提高了14.82%,比局部搜索算法（LSA）所获得任务收益提高了10.32%,并且同等条件下,采用GSA得到的观测图像的质量比其他两种方法得到的图像质量更高。实验结果表明,GSA在实际卫星调度中,能够有效地提高图像观测质量和任务观测收益。

英文摘要：

In order to solve the problem that observational image quality and profits are low in satellite scheduling by adopting lagged weather forecast cloud information, a mathematic model capturing real-time cloud distribution was proposed. The Agile Earth Observation Satellite （AEOS） scheduling model was also built based on the real-time cloud information. Considering the local optimization of Greedy Search Algorithm （GSA） and it can give full consideration for constraints such as cloud of satellite observation and limited storage resources, the applications of GSA for the satellite scheduling problem were researched. Firstly, the cloud coverage of observation task was considered in priority order by GSA. The image quality value of observation task was calculated according to the size of cloud coverage and the observation task was selected by the sort of the image quality value. Secondly, the task with the maximize profit was selected according to task size, deadline and satellite storage resource. Finally, satellite observation and task transmission were completed according to their ability of improving profit. The simulation experiments show that, on the case of 100 tasks, the task profit of satellite schedule adopting GSA was improved by 14.82% and 10.32% compared with the Dynamic Programming Algorithm （DPA） and Local Search Algorithm （LSA） respectively. Besides, the image quality of applying GSA is higher than taking DPA and LSA in the same circumstance. The experimental results show that the GSA can effectively improve the image observation quality and task observation profit of satellite scheduling.