Bayesian Detection and Classification for Space-Augmented Space Situational Awareness Under Intermittent Communications

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This paper examines the problem of detecting and classifying objects in Earth orbit using a Space-Augmented Space Surveillance Network (SA-SSN). A SA-SSN uses a combination of ground- and space-based sensors to monitor activities over a range of space orbits from low earth orbits up to an altitude higher than the geosynchronous orbit. The authors develop a cost-aware Bayesian risk analysis approach for object detection and classification, using range-angle sensors with intermittent information-sharing between the sensors. The problem is formulated in a simplified two-dimensional setting where the SA-SSN is composed of four ground-based sensors and a space-based orbiting sensor satellite.