Space Based Multi-Sensor Data Fusion to Quantify and Assess the Behavior of Earth-Orbiting Artificial Space Object Population

ABSTRACT: Given the national importance of protecting our space assets, there is a growing need for an effective means to track and characterize Earth orbiting space objects, including operational satellites, inoperational satellites, and debris.This, in turn, has motivated the development of space-based sensors that can observe the same objects or the same space regions at any time of the day or night, and in all-weather conditions.However, given the large number of space objects and vast distances involved, we need a systematic method to task these sensors to maximize the detection, reacquisition, and characterization of space objects.Under this Phase II effort, STR will expand the technology concept developed by its Phase I effort to maximize detection probability and state estimation accuracy of Earth orbiting objects through closed-loop data fusion with a feature-aided Multiple Hypothesis Tracker with the K-best hypothesis selection capability, coupled with the Sensor Resource Management to optimally assign tasks of discovering new objects and updating tracked objects to sensor resources, based on an information theoretic metric.We will develop a closed-loop system prototype, provide a demonstration of the closed-loop data fusion concepts for space surveillance systems by space-based sensors.; BENEFIT: Using space-based SSA platforms will enable almost continuous SSA operations, removing space and temporal gaps of space object observations by conventional ground-based Space Surveillance Network (SSN) optical sensors.The potential commercial applications include space monitoring applications, especially the monitoring of the commercial geostationary satellite operations, mainly broadcasting and communication applications.In the future, commercial SSA monitoring will become necessary for other private-sector space activities, including space exploration and sightseeing.