Roberto Furfaro, University of Arizona, Richard Linares, University of Minnesota, David Gaylor, University of Arizona, Moriba Jah, University of Arizona, Ramona Walls, Cyverse, University of Arizona
Keywords: Space Situational Awareness, Space Ontology, Bayesian Networks, Machine Learning
Abstract:
In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a “from data to discovery†paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.
Date of Conference: September 20-23, 2016
Track: SSA Algorithms