密集观测场景下的敏捷成像卫星任务规划方法

针对密集观测场景下敏捷成像卫星任务规划问题求解空间大、输入任务序列较长的特点，综合考虑时间窗口约束、任务转移时卫星姿态调整时间、存储约束和电量约束，对敏捷成像卫星任务规划问题进行建模. 提出融合IndRNN和Pointer Networks的算法模型（Ind-PN）对敏捷成像卫星任务规划问题进行求解，使用多层的IndRNN结构作为算法模型的解码器. 基于Pointer Networks机制对输入任务序列进行选择，使用Mask向量考虑敏捷成像卫星任务规划问题中的各类约束. 基于Actor Critic强化学习算法对算法模型进行训练，以获得最大的观测收益率. 实验结果表明，对于密集观测场景下的任务规划，Ind-PN算法的收敛速度更快，可以获得更高的观测收益率.

Agile imaging satellite task planning method for intensive observation

The agile imaging satellite task planning problem under intensive observation scenarios has the characteristics of large space and long input task sequence length. The agile imaging satellite task planning problem was modeled by considering the constraints of time windows, attitude adjustment time during task transfer, and satellite memory and power constraints. An algorithm model (Ind-PN) combining IndRNN and Pointer Networks was proposed to solve the agile imaging satellite task planning problem, and a multi-layer IndRNN structure was used as the decoder of the model. The input task sequence was selected based on Pointer Networks mechanism, and Mask vector was used to consider various constraints of the agile imaging satellite task planning problem. The algorithm model was trained by Actor Critic reinforcement learning algorithm in order to obtain the maximum observation reward rate. The experimental results show that Ind-PN algorithm converges faster and can achieve higher observation rate of reward for task planning under intensive observation scenarios.