Continuous monitoring scheduling for moving targets by Earth observation satellites

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.