Closely Spaced Object Detection Utilizing Spatial Information in Spectroastrometric Observations

The detection of closely spaced artificial satellites informs tactical decision making in a high risk scenario in the space domain. In regimes where spatial information is lost (ground observations of small or distant satellites), spectroastrometry simulations have demonstrated the potential to detect the presence of multiple objects down to 0′′.05–ten meters at geostationary orbit–using a medium resolution optical spectrograph on a large aperture telescope.1 This technique falls into the growing field of learned space domain awareness: leveraging convolutional neural networks to rapidly infer tactical information from complex, non-intuitive data. In this work we present a field rotation nodding technique that removes the need for a priori knowledge of the closely spaced object on sky orientation. We discuss modifications to an optical spectrograph necessary to perform this technique. We present simulated bounds on the effectiveness of spectroastrometry for the detection of closely spaced objects.