A Deep Reinforcement Learning Method for SAR Earth Observation Satellite Scheduling Problem

Synthetic aperture radar (SAR) Earth observation satellites (SEOS) leverage microwave imaging capabilities to enable all-weather, round-the-clock observation, making them increasingly valuable in various practical applications. The SEOS scheduling problem (SEOSSP) involves optimizing the sequence and timing of observation tasks while accounting for both task requirements and satellite capabilities. This study addresses the gap in existing research by thoroughly analyzing the SEOSSP, formulating a corresponding mathematical model, and proposing a solution using deep reinforcement learning (DRL). Experimen-tal results demonstrate that the proposed method can quickly produce high-quality solutions, highlighting its promise for future applications in SEOS scheduling optimization.