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Date of Award
Thesis - Open Access
Master of Science in Aerospace Engineering
Dr. Hamilton Hagar
Dr. Yechiel Crispin
Dr. Harihar Khanal
This research evaluates a probabilistic methodology for estimating the ability of satellite tracking networks to provide tracking and data acquisition services to large constellations of satellites. This approach, developed by Hagar is evaluated using Monte Carlo simulations of optimal satellite contact scheduling on a tracking network for a certain class of satellites. The actual results of the scheduled Monte Carlo simulations were then compared to the predicted values computed with Hagar's methodology for a range constellation and network sizes. Comparison methods include percent difference, a Wilcoxon signed ranks test and a Mann-Whitney U test.
The Monte Carlo simulations were run for only low earth orbit (LEO) satellites in circular orbit at random altitudes ranging from 180km to 1000km, and inclinations from near equatorial to near polar. For each Monte Carlo sample the orbit plane orientations and initial satellite positions were randomly generated. Ninety-six different cases were simulated and compared to their respective counterparts using the probabilistic approach. The results indicate that the probabilistic method is not finished. Although the method is fair in its approximation of network capabilities it lacks the accuracy to be used as a single tool for analysis of network capabilities. With additional research and adjustment the method could give satellite network users and planners a useful tool for predicting the ability of tracking and data acquisition networks to meet current and projected satellite tracking needs.
Scholarly Commons Citation
Stubbe, Matthew Lynn, "Evaluation of Probabilistic Methodology for Predicting Satellite Tracking Resources" (2006). Theses - Daytona Beach. 225.