  In the context of autonomous airships, several works in control and guidance
use wind velocity to design a control law. However, in general, this
information is not directly measured in robotic airships. This paper presents
three alternative versions for estimation of wind velocity. Firstly, an
Extended Kalman Filter is designed as a model-based approach. Then a Neural
Network is designed as a data-driven approach. Finally, a hybrid estimator is
proposed by performing a fusion between the previous designed estimators:
model-based and data-driven. All approaches consider only Global Positioning
System (GPS), Inertial Measurement Unit (IMU) and a one dimensional Pitot tube
as available sensors. Simulations in a realistic nonlinear model of the airship
suggest that the cooperation between these two techniques increases the
estimation performance.
