Convolutional neural networks (CNNs) are utilized for the task of inferring the attitude status in terms of rotation rate of resident space objects (RSOs) using simulated light curve measurements. Research into the performance of CNNs on synthetic light curve data-sets has shown significant promise that has not yet translated into success when working with empirically collected light curves. This limitation appears to be due to a number of factors including: mixing of bidirectional reflectance distribution function (BRDF) signatures, the effects of sensor noise, and blurring due to atmospheric turbulence. A synthetic data-set of approximately 7500 light curves was generated that takes into account realistic BRDF signatures and environmental parameters. The RSO used in this study was texture mapped with three unique material BRDF signatures: silicon solar panel, glossy paint, and aluminum. A two-step BRDF model inversion of the Beard-Maxwell model was performed using empirically collected data-sets of these materials in order to physically derive the BRDF model parameters. The CNN was trained on light curves resulting from the RSO performing four different maneuvers: tumbling, accelerating in rotational rate, stabilizing, and inactive (or stable in rotation rate). The CNN achieved an overall classification accuracy of 86.2% across the four maneuver classes. A confusion matrix analysis of the different classes of maneuvers suggested that our model performed best when classifying tumbling and accelerating RSOs (94% accuracy) and worst at classifying inactive RSOs (60% accuracy). This performance limitation when classifying inactive RSOs is attributed to (1) back-scatter signatures and specular glints within the synthetic light curves of inactive satellites being mistaken as attitude maneuvers, and (2) low signal-to-noise ratio due to factors such as atmospheric blurring. These results suggest that CNNs have strong potential for aiding in the problem of classifying satellite attitude status from light curves, but that machine learning research must focus on developing training sets and pre-processing techniques that account for these complications.
Convolutional Neural Networks for Inference of Space Object Attitude Status
J Astronaut Sci
The Journal of the Astronautical Sciences ; 69 , 2 ; 593-626
2022-04-01
34 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Convolutional Neural Networks for Inference of Space Object Attitude Status
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