Applications of artificial intelligence have been gaining extraordinary traction in recent years across innumerable domains. These novel approaches and technological leaps permit leveraging profound quantities of data in a manner from which to elucidate and ease the modeling of arduous physical phenomena. ExoAnalytic collects over 500,000 resident space object images nightly with an arsenal of over 300 autonomous sensors; extending the autonomy of collection to data curation, anomaly detection, and notification is of paramount improtance if elusive events are desired to be captured and classified. Efforts begin with rigorous image annotation of observed glints, streaking stars, and resident space obejcts; synthetic plumes were generated from both Generative Adversarial Networks as well as manual image augmentation techniques. Preliminary results permitted the successful classification of observed debris generated events from AMC-9, Telkom-1, and Intelsat-29e. After initial proof-of-concept, these events are incorporated into the training pipeline in order to characterize potentially unknown debris generating or anomalous events in future observations. The inclusion of a visual tracking system aides in reducing false alarms by roughly 30%. Future efforts include applications on both historical datamining as well as real-time indications and warnings for satellite analysts in their daily operations while maintaining a low probability of false alarm through detection and tracking algorithm refinement.
@article{CATARACTS,title={Real-Time Plume Detection and Segmentation Using Neural Networks},author={Temple, Dwight},year={2020},journel={The Journal of Astronautical Sciences},doi={10.1007/s40295-020-00237-w},}
2018
Synthetic Heterogeneous Anomaly and Maneuver - Neural Network Event Winnowing System
Dwight Temple
Adavanced Maui Optical and Space Surveillance Technologies Conference 2018
