Validation of Linear Covariance Techniques for Mars Entry, Descent, and Landing Guidance and Navigation Performance Analysis

Current Monte Carlo-based uncertainty analysis methods require significant computational resources to evaluate the performance of a closed-loop guidance, navigation, and control system. An attractive alternative, particularly during the preliminary and conceptual design phase, is linear covariance analysis, which can provide the same statistical information as Monte Carlo methods at a fraction of the computational cost. Linear covariance analysis methods have been demonstrated for in-space flight systems, but has only recently been applied to atmospheric flight. In this study, a six-degree-of-freedom formulation of both linear covariance and Monte Carlo analysis tools are used to assess a Mars entry, descent, and landing scenario. The scenario includes a complete guidance, navigation, and control system with algorithm, sensor, and effector models for both atmospheric gliding entry and powered descent flight phases to support precision landing. Comparison of the linear covariance and Monte Carlo results shows landed accuracy is approximated within 1% between the two frameworks.