ORCID Identifier(s)

0000-0002-8836-2449

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

Comments

Degree granted by The University of Texas at Arlington

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