Stefan Scharring, German Aerospace Center (DLR); Tristan Meyer, German Aerospace Center (DLR); Nils Bartels, German Aerospace Center (DLR); Wolfgang Riede, German Aerospace Center (DLR)
https://doi.org/10.64861/JVFM2041
Keywords: Light Curve, Photometry, Light Curve Inversion, Rotational State, Epoch Method, Photometric Pattern Method, Quanta Photogrammetry (QPM), Space Domain Awareness (SDA), Space Situational Awareness (SSA), Low Earth Orbit (LEO)
Abstract:
Photometric characterization of resident space objects (RSO) in the Low Earth Orbit (LEO) is an important enabler for the reconnaissance of unidentified satellites in space domain awareness (SDA) providing insight to an unknown object’s operational status and improving the prediction of its orbit and rotational state. Moreover, future missions for active debris removal can benefit from such a characterization in order to assess the feasibility of robotic capture. In this regard, light curves stemming from sunlight reflections off an orbital target, as observed from the ground, can be utilized for target characterization in terms of attitude, rotation, and shape, despite the fact that the light curves themselves do not contain any kind of spatially resolved data, but only the temporal course of the recorded intensity. In the field of light curve inversion, many methods aiming for the extraction of target information are based on at least a few rudimentary initial assumptions which characterize the object. In contrast, our study combines so-called agnostic methods for the purpose of target reconnaissance via multiple light curve observations. Focusing on light curves that exhibit approximate periodicity, we restrict ourselves to the single hypothesis of a rotating target in flat-spin mode, i.e., the orientation of its rotational axis remains constant over the observation timespan that is considered for analysis, in particular, if multiple observations are employed. Beyond that, no further knowledge on, e.g., the object’s shape or material composition, is used, but only the corresponding position data of the observation geometry during light curve observation is required. Based on temporally resolved periodicity analysis of the light curves via self-correlation, the temporal course of the observed synodic period is extracted as the starting point of our proposed data analysis pipeline. This data is used to derive probable solutions for the direction of the rotation axis independently via the epoch method (Hall et al., 2006) and the photometric patterns method (Koshkin et al., 2024). As these methods typically yield multiple possible solutions for the direction of the rotation axis, cross-validation using both methods is performed to derive an unambiguous result. In the body-fixed frame of the target, the epoch method relies on a shift of the longitude of the phase angle bisector (PAB), while the photometric patterns method identifies characteristic light curve segments corresponding to a specific latitude of the PAB vector. Beyond a mere comparison of the methods’ solution, this allows for a deep consistency check of the underlying geometric implications that are associated with the results from the different methods. Moreover, the outcome from the geometric analysis of this cross-validation is used to derive an angularly resolved photogram (or at least parts thereof) of the observed object via Quanta Photogrammetry (Kucharski et al., 2020), which bears the potential for automated target classification via machine-learning algorithms. The application potential as well as the constraints of this analysis pipeline for target reconnaissance are explored and validated using synthetically light curves of exemplary satellites and rocket bodies which are generated by means of our raytracing-based code Raxus Prime.
Date of Conference: September 15-18, 2026
Track: Satellite Characterization