A station-keeping maneuver detection method of non-cooperative geosynchronous satellites

Abstract

Orbit maneuver detection is one of the major concerns for the work of Space Situation Awareness of GEO protected region. In this study, we propose a non-cooperative GEO satellites station-keeping maneuver detection approach based on the two-dimensional Convolutional Neural Network (CNN) and the public Two-Line Element (TLE). The proposed method focuses on identifying “station-keeping categories” using the time series segments of TLE data. The time series TLE of the selected objects are converted into several equal-sized segments using a window slicing method. These segments are then used to train a two-dimensional CNN classification model that can divide the input data into three categories of maneuver. The applicability of the proposed method on satellites in the same orbital region were further tested using other objects that had not been included during the modeling process. Experimental results demonstrated the feasibility and robustness of the proposed method, with a classification macro F1-score of 94.45%, macro precision of 94.48%, and macro recall of 94.46%. The results suggest that the proposed method provides a promising solution for detecting GEO station-keeping maneuvers.