Multistage-Fusion Algorithm for Estimation of Aerodynamic Angles in Mini Aerial Vehicle.

This research work is inspired by the fact that a mini aerial vehicle with low-aspect-ratio wings (aspect ratio less than 2) has nonlinear lift curves and does not stall sharply, as compared with high-aspect-ratio wings. This paper presents a data fusion algorithm for estimating the aerodynamic angles in a mini aerial vehicle that has low-aspect-ratio wings. The true states of the aircraft motion are generated using a flight simulation program, and a zero-mean white noise is added to a few of these states for sensor simulation. Using the simulated sensors, in the first stage, Euler angles are estimated using an extended Kalman filter algorithm. The estimated Euler angles are further used with accelerometer, angular rates, magnetometer measurements, and the airspeed V for estimation of aerodynamic angles at higher angles of attack. This paper also proposes a novel pseudoestimation technique and also discusses three different schemes of estimation of angle of attack and sideslip angle. Effect of drift in gyro measurements in the angle-of-attack and sideslip-angle estimation is studied. When sensor bias/drift is present, it was observed that there is a bias of more than 10 deg present in the estimated aerodynamic angles. A new modification is proposed in which the pseudoestimation of angle of attack and sideslip angle is used as measurement in the second-stage extended Kalman filter. This reduces the estimation bias and the mean error in both angle of attack and sideslip angle, and it is below 1 deg. [ABSTRACT FROM AUTHOR]