Dissertation/Thesis Abstract

Bayesian estimation for tracking of spiraling reentry vehicles
by Tapiero Bernal, Juan E., M.S., Marquette University, 2013, 86; 1534305
Abstract (Summary)

This thesis presents a development of a physics-based dynamics model of a spiraling atmospheric reentry vehicle. An analysis of the trajectory characteristics, using elements from differential geometry lead to a relationship of the state of the vehicle to the spiraling of motion. The Bayesian estimation framework for nonlinear systems is introduced showing the theoretical basis of the estimation techniques. Two estimation algorithms, extended Kalman filter and particle filter are presented, their mathematical formulation and implementation characteristics.

Different trajectories that can be represented by the model are introduced and analyzed, showing the spiraling behavior that can be described by the model. The extended Kalman filter and particle filter are compared in the ability to estimate the states and spiraling characteristics, with successful results for both techniques inside one standard deviation. In general superior performance was shown by the particle filter, which estimated the torsion with an error 10 orders of magnitude smaller.

Indexing (document details)
Advisor: Bishop, Robert H.
Commitee: Spiller, Elaine, Yaz, Edwin
School: Marquette University
Department: Electrical & Computer Engineering
School Location: United States -- Wisconsin
Source: MAI 51/05M(E), Masters Abstracts International
Subjects: Statistics, Computer Engineering, Aerospace engineering
Keywords: Estimation, Extended kalman filter, Modeling, Particle filter, Reentry, Tracking
Publication Number: 1534305
ISBN: 978-1-267-93675-2
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