First Approach to Coupling of Numerical Lifting-Line Theory and Linear Covariance Analysis for UAV State Uncertainty Propagation

Numerical lifting-line is a computationally efficient method for calculating aerodynamic forces and moments on aircraft. However, its potential has yet to be tapped for use in guidance, navigation, and control (GN&C). Linear covariance analysis is becoming a popular GN&C design tool and shows promise for pairing with numerical lifting-line. Pairing numerical lifting-line with linear covariance analysis allows for forward propagation of state uncertainty for real-time decision making. We demonstrate this for select state variables in a drone aerial recapture situation. Linear covariance analysis uses finite difference derivatives obtained from numerical lifting-line to calculate force and moment variances. These show agreement with Monte Carlo simulation results to within 10%, without the significant computational cost of Monte Carlo. These results show numerical lifting-line can be used in linear covariance analysis of an entire UAV GN&C solution. Not only does this allow for real-time uncertainty propagation, but also faster and more thorough multi-disciplinary design optimization