UAV Attitude, Heading, and Wind Estimation Using GPS/INS and an Air Data System

Robust path following using a small fixed-wing airplane for aerial research

“…As another method, a wind field estimation comparing measures of aircraft motion with respect to predictions of the dynamical model can be done [11], [12], [13], but it has as a drawback the need of certain navigation equipments and the constraint that precision relies on noisy measurements. These measurements can be filtered like in [14], but errors will always depend on each sensor accuracy. Furthermore, wind can be managed as a disturbance, as in [6], [15], which is the approach used in this paper, described in Section III.…”

Tightly Coupled Navigation and Wind Estimation for Mini UAVs

“…15,17,18 We stress that for a same type of filter, the results between this approach and the above di↵er since: i) linearity is introduced in the 1D Pitot measurement (15) in place of GPS speed (11) which is rewritten as…”

“…7 With an added Pitot tube, Cho et al 16 design a relatively simple nonlinear Kalman filter for horizontal wind estimation. Recent works [17][18][19] expand upon this concept to include vertical wind, incorporate Angle-ofAttack (AoA) and Angle-of-Sideslip (AoS) which both relate the aircraft body frame to the wind frame, and then uses Kalman filters to estimate attitude, ground speed and wind components. Such techniques require the capability to accurately determining attitude and speeds from various cheap sensors such as Global Positioning System (GPS), Inertial Measurement Unit (IMU) and magnetometer which is related to the UAV navigation problem, a well developed field [20][21][22][23][24] in which, however, most existing works implicitly assume that the aircraft is always pointing in the direction of its total velocity, i.e., its AoA and AoS are negligible.…”

“…Finally, validating such wind estimators in experimental flight is challenging due to the di culty in accurately measuring the wind. One can yet use ground weather station data, radiosondes and piloted balloons, or SOnic Detection and Ranging, 8,[14][15][16][17] albeit only an approximation to the local wind at the aircraft.…”

Aircraft model-independent airspeed estimation without pitot tube measurements

“…Various wind estimation approaches have been considered using direct methods [26], simple filtering [27], iterative optimization [28], and nonlinear Kalman filtering [29,30]. Each of these approaches takes advantage of pitot tube airspeed information in order to relate airspeed and ground speed and properly extract the wind, i.e., if two components in the wind velocity triangle are known, you can extract the third.…”