The studies of earth observation satellite (EOS) scheduling for stationary targets have been increasing rapidly in recent years. However, these studies ignore EOS scheduling for moving targets (EOSSMT), which is urgently needed in many situations. EOSSMT is more complicated due to two factors, 1) location prediction of the moving targets and 2) algorithm design of EOSSMT optimization model for highly-nonlinear characteristics. In this article, we present a novel continuous monitoring scheduling methodology for moving targets by EOSs. Firstly, in order to predict the location of moving targets, a prediction-capture method, which can calculate the capture probability (CP) of the targets, is proposed. Then, based on the concept CP, we regard the EOSSMT as a stochastic integer nonlinear programming problem. Aiming to maximize observation times and duration, two objective functions with low complexity are proposed correspondingly. Finally, to solve our highly-nonlinear optimization model more efficiently, a dispersion-based heuristic (DBH) is proposed. In the computational experiments, a number of instances (scenarios), in which moving targets are successfully observed, verify the correctness and efficiency of our novel methodology for EOSSMT. Computational experiments also show that DBH can achieve better results than the Genetic Algorithm and Greedy Algorithm, with significantly less algorithm complexity.

静止目标对地观测卫星调度研究近年来增长迅速。然而，这些研究忽略了在许多情况下迫切需要的移动目标 EOS 调度 (EOSSMT)。由于两个因素，EOSSMT 更加复杂，1）移动目标的位置预测和 2）针对高度非线性特性的 EOSSMT 优化模型的算法设计。在本文中，我们提出了一种新颖的 EOS 移动目标连续监控调度方法。首先，为了预测运动目标的位置，提出了一种预测捕获方法，该方法可以计算目标的捕获概率（CP）。然后，基于 CP 的概念，我们将 EOSSMT 视为一个随机整数非线性规划问题。以最大化观察次数和持续时间为目标，相应地提出了两个复杂度较低的目标函数。最后，为了更有效地解决我们的高度非线性优化模型，提出了基于分散的启发式 (DBH)。在计算实验中，成功观察到移动目标的许多实例（场景）验证了我们用于 EOSSMT 的新方法的正确性和效率。计算实验也表明，DBH 可以取得比遗传算法更好的结果，贪心算法，算法复杂度显着降低。