Graduation Semester and Year
2018
Language
English
Document Type
Thesis
Degree Name
Master of Science in Aerospace Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Kamesh Subbarao
Abstract
In this thesis, novel observability-measure based sensor tasking methods are studied for satellite tracking applications. The tasking is performed by first computing the Hellinger Distance between ground/space based sensors and space objects, and then the measure is utilized for selecting the sensors that maximize observability. Several other measures such as the Fisher Information Gain, Largest Lyapunov Exponent, and Shannon Information Gain have also been utilized and the performance of these measures are computed against each other. The object's state estimates are obtained using nonlinear estimation techniques. The Extended Kalman Filter, Unscented Kalman Filter, and Bootstrap Particle Filter are compared within in this framework. Representative numerical simulations are performed to evaluate the efficacy of the new tasking approach. The proposed tasking approach is also compared with some baseline approaches.
Keywords
Satellite tracking, Sensor tasking, Stochastic observability, Observability, Hellinger distance, Extended Kalman Filter, EKF, Unscented Kalman Filter, UKF, Bootstrap Particle Filter, BPF, Space object, Estimation, Tasking
Disciplines
Aerospace Engineering | Engineering | Mechanical Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
McDonald, Mitchel T., "Sensor Tasking for Satellite Tracking Utilizing Observability Measures" (2018). Mechanical and Aerospace Engineering Theses. 838.
https://mavmatrix.uta.edu/mechaerospace_theses/838
Comments
Degree granted by The University of Texas at Arlington