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.
|Advisor:||Bishop, Robert H.|
|Commitee:||Spiller, Elaine, Yaz, Edwin|
|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|
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