Deep Learning Enabled Uncorrelated Space Observation Association
This addresses the needle-in-a-haystack challenge of space observation association for astronomy and satellite tracking, representing an incremental improvement by applying deep learning to a known bottleneck.
The paper tackled the problem of associating uncorrelated optical space observations to identify groups from the same resident space objects, showing that a deep learning model without physics knowledge achieved 83.1% accuracy on balanced pairs and identified 111 out of 142 objects in a demonstration set while exploring only 0.06% of the search space.
Uncorrelated optical space observation association represents a classic needle in a haystack problem. The objective being to find small groups of observations that are likely of the same resident space objects (RSOs) from amongst the much larger population of all uncorrelated observations. These observations being potentially widely disparate both temporally and with respect to the observing sensor position. By training on a large representative data set this paper shows that a deep learning enabled learned model with no encoded knowledge of physics or orbital mechanics can learn a model for identifying observations of common objects. When presented with balanced input sets of 50% matching observation pairs the learned model was able to correctly identify if the observation pairs were of the same RSO 83.1% of the time. The resulting learned model is then used in conjunction with a search algorithm on an unbalanced demonstration set of 1,000 disparate simulated uncorrelated observations and is shown to be able to successfully identify true three observation sets representing 111 out of 142 objects in the population. With most objects being identified in multiple three observation triplets. This is accomplished while only exploring 0.06% of the search space of 1.66e8 possible unique triplet combinations.