Deep Learning for Communication-Limited Spacecraft
Description
The objective of this project is to investigate the feasibility of applying deep-learning algorithms to communication-limited spacecraft, an operational domain where a slow, restricted, or intermittent downlink bottleneck inhibits the generation of large training datasets on the ground. With novel complex sensors generating ever-increasing amounts of data, it is imperative to be able to autonomously and robustly classify scientifically useful data to maximize scientific utility per bit transmitted to the ground. This project studies two classification approaches, including supervised transfer learning and unsupervised feature extraction followed by clustering, to optimize selection of data products for download.
Detailed example
Predictions
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Deep Learning for Communication-Limited Spacecraft
Controls / human review
ATO: Not reported; PIA: Not published