Space Signatures for Rapid Unambiguous Identification of Satellites

Many of today's sensors collect various data types beyond the traditional radiometric (range) or photometric (angles) that we call Space Object Identification (SOI) data. These data sources can yield discriminating satellite features and present a clear opportunity for correlation techniques to provide POI and improved track custody. We can use light reflectivity magnitude profiles and inverse synthetic aperture radar imaging to model spacecraft attitude. Heat signature profiling may be established with IR sensing as objects ascend and descend to/from Earth eclipsing. Maneuver models and profiling may be obtained as objects station-keep and perform momentum dumps. Multi-color and/or hyperspectral photometry may be used to infer materials of the satellite's composition. RF transmissions may be analyzed spectrally to characterize what frequencies and coding techniques are used. We propose a new approach to data correlation. Our Phase I effort will research and design a prototype Bayesian discrimination framework to object identification and recognition. As an initial form of representative SOI data, we will develop an application to generate predictive optical magnitude (light curve) data, representative of actual observational data. We will modify a Multiple Model Adaptive Estimator (MMAE) approach to show how our core Bayesian discriminator concepts can efficiently and rapidly improve positive identification of catalogued (modeled) and un-catalogued (un-modeled) space objects. In addition, we will develop a tree-based taxonomy of representative 3D models to represent a variety of alternatives for the Bayesian discriminator. Finally, we will investigate the availability and accessibility of SOI data sources for future incorporation to the Bayesian discriminator for a possible Phase II follow-on effort.