Identifying Aerodynamics of Delta-Wing Drones for Model-Based Navigation: A Comparative Study

Abstract

This paper presents a comparative analysis of two methodologies for estimating unknown parameters in a Vehicle Dynamic Model (VDM)-based sensor fusion framework for small drones. Focusing on a delta-wing drone, we conduct open-air wind tunnel experiments to determine a functional aerodynamic model. Subsequently, we compare two methodologies for unknown model parameters identification, one based on linear regression on wind tunnel experimental data, and the other employing partial-update-based estimators on recorded flight data. The performance of both parameter estimation approaches is then evaluated in a VDM-based framework through three independent test flights. Our results highlight the necessity of wind tunnel experiments for aerodynamic model formulation, while the data-driven method proves useful to identify the parameters at a low cost. Furthermore, we demonstrate that both (flight) data-driven and wind-tunnel experiment-based identified aerodynamics significantly enhance positioning accuracy, particularly in the absence of satellite signals, when integrated with low-cost consumer-grade MEMS inertial sensors.

Keywords

• Aerodynamics
• delta-wing drone
• extended Kalman filter
• model-based navigation
• observability Gramian
• Schmidt-Kalman filter