Michael Howard, Charles River Analytics, Inc., Bernie Klem, SASSO, Joe Gorman, Charles River Analytics, Inc.
Keywords: Non-resolved Object Characterization, GEO Satellite Characterization
Abstract:
Object characterization is the description of a resident space object (RSO), its capabilities and its behavior. While astrometric data has been used extensively for object detection, location, and characterization, photometric data has been less widely applied and remains a promising area for improving RSO characterization. RSO characteristics which may influence changes in light intensity with respect to changes in viewing angle or orientation signature include geometry, orientation, components material properties, stability and other characteristics. However, most RSO characterization is presently performed manually and on an individual basis by space analysts and there is a need for efficient and automated methods to perform characterization.
This paper discusses the application of machine learning techniques to characterization of RSOs in the geosynchronous altitude regime using photometric data. We develop simulated signatures in the visible spectral band of three basic RSO types, with variations in object orientation, material characteristics, size and attitude and attempt to recover these properties through object characterization techniques. We generate observations by sampling noisy measurements from the simulated signature. Next, we extract a set of features from the observations and train machine learning algorithms to classify the signatures. We consider the effectiveness of a set of binary classifiers trained to individually recognize separate cases. The results of each classifier are combined together to produce a final output characterization of an input observation. Experiments with varying levels of noise are presented, and we evaluate models with respect to classification accuracy and other criteria. The end result of this process is a unique methodology for exploiting the use usefulness and applicability of machine learning to an important space sensing and identification process.
This material is based upon work supported by the United States Air Force under Contract No. FA9453-14-M-0153.
Date of Conference: September 15-18, 2015
Track: Poster