Dr. Shen Xin’s research primarily focuses on intelligent schedule of remote sensing satellites, including the following key areas:

1. Satellite mission planning and optimization for complex imaging modes: Targeting the “dynamic imaging” capability of new-generation super-agile satellites, he has investigated mission planning modeling methods for same-orbit multi-point targets, multi-strip stitching, and stereo imaging, so as to meet the integrated optimal scheduling requirements of multi-mode imaging.

2. Multi-satellite coordination and constellation resource scheduling: Addressing typical application needs such as regional coverage, large-area mapping, and emergency observation, he has developed multi-satellite joint mission planning models to enable efficient coordination of constellation resources, thereby promoting a systematic upgrade from single-satellite to constellation-level operation and control capabilities.

3. Deep integration of intelligent optimization algorithms and mission scheduling: In recent years, his research has mainly focused on advanced AI methods such as deep reinforcement learning and meta-reinforcement learning to solve large-scale satellite constellation mission optimization problems, with the goal of establishing a new paradigm of “data-driven + heuristic” intelligent operation and control.

4. Development of in-orbit satellite ground systems and on-orbit validation: As the principal investigator of the ground TT&C system for the Luojia series satellites, he has supported the in-orbit telemetry, tracking and operation control of multiple Luojia satellites, and has undertaken mission planning development tasks for ground systems of several commercial satellite projects.