Publications Details

Linear Covariance Analysis Framework for Aerospace Vehicle Trajectory Modeling and Parametric Design

Heflin, Lucas B.; Zuiker, Nicholas J.; Calkins, Grace E.; Putnam, Zachary R.; Whitten, William D.

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.