Reinforcement Learning Driven Time-Sensitive Moving Target Tracking of Intelligent Agile Satellite

The evolution of satellite surveillance technology, coupled with advanced on-board intelligent systems and improved attitude maneuver capabilities, has positioned mission scheduling and execution as a prominent and active research area in recent years. With the urgent need for mission scheduling and execution to evolve from the static ground target to the time-sensitive moving target, the existing satellite often yields unsatisfactory results when managed through conventional manual methods for continuously tracking a time-sensitive moving target. In response to this challenge, the article introduces a reinforcement learning (RL) driven method enabling the satellite to schedule and execute the tracking of time-sensitive moving target autonomously. In the article, we conduct a meticulous analysis of the target tracking process, which we decompose into three distinct subphases and address it utilizing attitude control, elucidating the integration of our model with satellite attitude dynamics and kinematics. Besides, we propose flight trajectory prediction module to predict the future trajectory position and minimize the position error due to the communication latency. With the dual aim of expeditiously tracking the target and concurrently enhancing payload stability, a well-designed reward function, which is applied to various RL algorithms, effectively governs satellite attitude control to achieve accurate and stable tracking of moving target. In addition, through the eight distinct tracking scenarios simulated in our proposed satellite target tracking environment, our extensive experiments and ablation studies consistently underscore the advantages and efficacy of our proposed method. This work serves as a foundational cornerstone for future research endeavors focused on tracking multiple time-sensitive moving targets within extensive and complex intelligent satellite constellation.