Date of Award
Doctor of Philosophy (PhD)
Aerospace Engineering Sciences
The use of near Earth space has increased dramatically in the past few decades, and operational satellites are an integral part of modern society. The increased presence in space has led to an increase in the amount of orbital debris, which poses a growing threat to current and future space missions. Characterization of the debris environment is crucial to our continued use of high value orbit regimes such as the geosynchronous (GEO) belt. Objects in GEO pose unique challenges, by virtue of being densely spaced and tracked by a limited number of sensors in short observation windows. This research examines the use of a new class of multitarget filters to approach the problem of orbit determination for the large number of objects present. The filters make use of a recently developed mathematical toolbox derived from point process theory known as Finite Set Statistics (FISST). Details of implementing FISST-derived filters are discussed, and a qualitative and quantitative comparison between FISST and traditional multitarget estimators demonstrates the suitability of the new methods for space object estimation. Specific challenges in the areas of sensor allocation and initial orbit determination are addressed in the framework. The sensor allocation scheme makes use of information gain functionals as formulated for FISST to efficiently collect measurements on the full multitarget system. Results from a simulated network of three ground stations tracking a large catalog of geosynchronous objects demonstrate improved performance as compared to simpler, non-information theoretic tasking schemes. Further studies incorporate an initial orbit determination technique to initiate new tracks in the multitarget filter. Together with a sensor allocation scheme designed to search for new targets and maintain knowledge of the existing catalog, the method comprises a solution to the search-detect-track problem. Simulation results for a single sensor case show that the problem can be solved for multiple objects with no a priori information, even in the presence of missed detections and false measurements. Collectively, this research seeks to advance the capabilities of FISST-derived filters for use in the estimation of geosynchronous space objects; additional directions for future research are presented in the conclusion.
Gehly, Steven, "Estimation of Geosynchronous Space Objects Using Finite Set Statistics Filtering Methods" (2016). Aerospace Engineering Sciences Graduate Theses & Dissertations. 148.