Pasquale Longobardi

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Aerodynamic characterization of a delta-wing UAV based on real flight data

Gabriel François Laupré, Pasquale Longobardi, Jan Skaloud

Vehicle Dynamic Model based navigation represents a novel approach to autonomous navigation for small UAVs. It significantly improves the navigation solution under GNSS outage condition without adding additional sensors to the platform. This paper proposes an alternative method for determination of the aerodynamic coefficients characterizing the system dynamics needed to enable VDM-based navigation. It relies on the usage of post-processed traditional INS/GNSS navigation solution for the estimation of initial values of the aerodynamic coefficients. The importance of optimal selection of experimental data is highlighted, proposing an evaluation criterion whose benefit is the increase of the statistical significance of the coefficient estimation.

IEEE2021

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This paper presents the first practical results of a model-based approach to autonomous navigation applied to a small delta wing drone. The aerodynamic coefficients of the considered platform are unknown and need to be determined for the (vehicle dynamic) model-based navigation to work properly. The proposed approach uses post-processed INS/GNSS trajectory estimates of relatively high precision as observations to refine priors'' of aerodynamic coefficients via state-estimation (Extended Kalman Filter). Two methods to derive such priors'' (i.e., initial parameter values) are investigated. The first adapts coefficients described in the literature for an aircraft of similar geometry. The second performs regression analysis of flight data to estimate coarse values of the coefficients. Both sets of coefficients are further re-calibrated in-flight via state estimation. The accuracy of the coefficient calibration is evaluated by simulating a GNSS outage of several minutes, during which the trajectory flown under autonomous navigation is compared to that of the reference.

2021

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