Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

Abstract

Earth observation satellites (EOSs) are specially equipped with remote sensing instruments to acquire images. In practical EOS scheduling, the uncertainty of cloud coverage is inevitable. We are the first to address robust EOS scheduling under uncertainty due to cloud coverage where the objective function aims to maximize the entire observation profit. We provide a robust formulation of the scheduling problem on the basis of a budgeted uncertainty set, while preserving the formulation linearity. A column-generation-based heuristic is also developed, in which the scheduling decisions in each satellite orbit are represented as columns. Eventually, a high-quality feasible solution is obtained using the generated columns. Extensive simulations are conducted on the basis of one of China's EOS constellations. The results indicate that the average optimality gap is less than 5%, which validates the performance of the proposed heuristic.