Title:
Uncertainty Quantification for Mars Entry, Descent, and Landing Reconstruction Using Adaptive Filtering

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Dutta, Soumyo
Braun, Robert D.
Karlgaard, Christopher D.
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Abstract
Mars entry, descent, and landing (EDL) trajectories are highly dependent on the vehicle's aerodynamics and the planet's atmospheric properties during the day-of- flight. A majority of previous EDL trajectory and atmosphere reconstruction analyses do not simultaneously estimate the flight trajectory and the uncertainties in the models. Adaptive filtering techniques, when combined with the traditional trajectory estimation methods, can improve the knowledge of the aerodynamic coefficients and atmospheric properties, while also estimating a realistic confidence interval for these parameters. Simulated datasets with known truth data are used in this study to show the improvement in state and uncertainty estimation by using adaptive filtering techniques. Such a methodology can then be implemented on existing and future EDL datasets to determine the aerodynamic and atmospheric uncertainties and improve engineering design tools.
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2013-01
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