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Completed TRL 3 (started at 2, targeting 3)
In recent years, there has been a large increase in the amount of orbital debris around Earth. With debris traveling at speeds upwards of 17,000 miles per hour, objects as small as paint flecks can pose a threat to personnel, vehicles, and satellites. For this reason, characterizing and tracking debris is becoming more and more important to maintaining the safety of space-based assets. The orbits of debris depend on the shape, material properties, and other characteristics of the debris through drag and solar radiation pressure. For this reason, it is desirable to determine characteristics of debris objects via observation in order to better predict their orbits. The goal of this research is to investigate the use of light curve data from multiple observers in order to estimate space object geometry. Multiple-model adaptive estimation approaches and neural networks can be used to simulate multiple candidate geometries, and together with light curve data, determine the most likely geometry. Implementing these techniques, adapted to accommodate multiple observers, will help NASA improve mission safety through collision avoidance with increased knowledge of debris locations and orbits.
Multiple-model adaptive estimation approaches and neural networks can be used to simulate multiple candidate geometries, and together with light curve data, determine the most likely geometry. Implementing these techniques, adapted to accommodate multiple observers, will help NASA improve mission safety through collision avoidance with increased knowledge of debris locations and orbits.
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