Research Progress on Estimating Space Objects Characteristics Using Ground-based Observation Data

With the rapid increase in space activities, the proliferation of space debris from space objects’ breakup and collisions poses catastrophic risks to orbital operations. Consequently, the monitoring and characterization of space objects—including their attitude, shape, and material properties—have become critical for target identification, collision avoidance, and active debris removal. This study systematically reviews relevant technical papers from recent proceedings of the AMOS Conference, a key academic forum in space situational awareness. The analysis encompasses ground-based observational data applications in space object characterization, attitude estimation, shape reconstruction, attitude evolution, and machine learning-assisted decision-making. These methodologies provide a comprehensive toolkit for the integrated analysis of space objects, offering valuable insights for future advancements in characterization technologies. Against the current situation and trend of increasingly abundant data related to characteristic estimation and increasingly mature propagation algorithms, this paper proposes a new idea that China should establish a systematic space target characteristic estimation mechanism.