@inproceedings{a694cbf28d7f4ccb9f13b8973545217d,
title = "Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design",
abstract = "This paper presents the Symbolic Linear Covariance Analysis Tool (SLiC), a Python framework capable of simplifying the construction, verification, and analysis of aerospace systems using linear covariance analysis techniques. The framework leverages open-source libraries to enable symbolic manipulation and object-oriented abstraction to remove many of the barriers to linear covariance analysis when compared to other methods. The benefits of linear covariance analysis with Monte Carlo verification are addressed and the framework design is described. The framework is validated against existing literature results and demonstrated for a sample aerospace use case of a hypersonic entry system.",
author = "Heflin, {Lucas B.} and Zuiker, {Nicholas J.} and Calkins, {Grace E.} and Putnam, {Zachary R.} and Daniel Whitten",
note = "Publisher Copyright: {\textcopyright} 2022, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.; AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 ; Conference date: 03-01-2022 Through 07-01-2022",
year = "2022",
doi = "10.2514/6.2022-2276",
language = "English (US)",
isbn = "9781624106316",
series = "AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA SciTech Forum 2022",
}