Fri Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Li, Zhen, Deng, Yunlong, Shi, Chuang, Guo, Qikai, and He, Zhenghang, 2026. Space object classification based on non–conservative force. Advances in Space Research, 77(6):7067–7085, doi:10.1016/j.asr.2026.01.080.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026AdSpR..77.7067L,
author = {{Li}, Zhen and {Deng}, Yunlong and {Shi}, Chuang and {Guo}, Qikai and {He}, Zhenghang},
title = "{Space object classification based on non-conservative force}",
journal = {Advances in Space Research},
keywords = {space object classification, Two-Line Element (TLE), Non-conservative force (NCF), Feature extraction, Feature selection},
year = 2026,
month = mar,
volume = {77},
number = {6},
pages = {7067-7085},
abstract = "{The increasingly crowded space environment necessitates enhanced Space
Situational Awareness (SSA) capabilities. In the SSA system, the
essential task is to classify space objects such as operational
satellites and defunct debris for various purposes.Traditional
approaches often rely on single observational data like light
curve, radar cross section, or optical image. However, orbital
parameters, which are updated frequently and cover a much larger
population of resident space objects, provide a complementary
and information-rich data source. In this study, we explore a
novel approach for space object classification based on orbital
parameters. We first derive the Non-Conservative Force (NCF)
accelerations from the orbital parameters and then extract a set
of features from the NCF time series. These features are
subsequently used to train several conventional classification
algorithm, including support vector machine, k-nearest
neighbors, and decision tree. The accuracy of the NCF
accelerations is validated using accelerometer measurements from
the GRACE-FO C satellite. Our experimental results demonstrate
that decision tree achieves an accuracy of 87.51\% in
distinguishing different categories of space objects based on
combinations of RCS size and object type. This indicates that
the proposed approach has significant potential for improving
classification in SSA systems.}",
doi = {10.1016/j.asr.2026.01.080},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026AdSpR..77.7067L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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