Characterization of space objects attitude motion using Lomb-Scargle Periodogram

Characterizing Earth-orbiting objects is crucial for Space Domain Awareness. Light curves, whichmeasureobject brightness over time, provide valuable information for this task. However, resulting from combination of several factors, their inversion is quite challenging. Different methods address this problem, including data fusion techniques, frequency spectra analysis, and multi-objective optimization. This paper proposes anarchitecture that employs the Lomb-Scargle Periodogram algorithm to extract frequency spectra from light curves and classify attitude motion based on their characteristics. Performance is evaluated using synthetic light curves generated by a numerical simulator which allows accounting for complex target shape, observation geometry, multi-temporal and multi-sensor measurements.