Data Reduction for Diverse Optical Observers through Fundamental Dynamic and Geometric Analysis

• Main Author: Sease, Bradley Jason
• Other Authors: Aerospace and Ocean Engineering
• Format: Others
• Published: Virginia Tech 2016
• Subjects: Space Situational Awareness; Optical Sensing; Orbit Estimation
• Online Access: http://hdl.handle.net/10919/70923

Typical algorithms for processing unresolved space imagery from optical systems make broad assumptions about the expected behavior of the sensors during collection. While these techniques are often successful at data reduction for a particular mission, they rarely extend to sensors in different operating modes. Such specialized techniques therefore reduce the number of sensors able to contribute imagery. By approaching this problem with analysis of the fundamental dynamic equations and geometry at play, we can gain a deeper understanding into the behavior of both stars and space objects viewed through optical sensors. This type of analysis has the potential to enable data collection from a wider variety of sensors, increasing both the quantity and quality of data available for space object catalog maintenance. This dissertation will explore the implications of this approach to unresolved data processing. Sensor-level motion descriptions will be derived and applied to the problem of space object discrimination and tracking. Results of this processing pipeline as applied to both simulated and real optical data will be presented. === Ph. D.