Abstract: 　　
    With the increase of space activities and the rapid growth in the number of space objects, the fragmentation and collision of these objects have generated large amounts of space debris, potentially leading to catastrophic consequences. As such, the monitoring and characterization of space objects have become crucial. Information about the geometric, kinematic, and material properties of these objects is critical for target identification, collision avoidance, and active debris removal. The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference, a prominent academic platform in the field of space situational awareness, has brought together cutting-edge research on the characterization of space objects. This study systematically reviews and summarizes relevant technical papers presented at the AMOS Conferences from 2016 to 2023. These papers explore the application of ground-based observation data in the characterization of space objects, covering topics such as attitude estimation, shape estimation, attitude evolution, and machine learning-assisted decision-making. Together, they provide a wealth of technical approaches and estimation methods that contribute to the comprehensive analysis of space objects and offer valuable insights for advancing future characterization techniques. In light of the increasing availability of data related to object characterization and the growing maturity of inversion algorithms, this paper proposes a new strategy for establishing a systematic framework for target characteristics estimation in China.