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In the context of autonomous airships, several works in control and guidanceuse wind velocity to design a control law. However, in general, thisinformation is not directly measured in robotic airships. This paper presentsthree alternative versions for estimation of wind velocity. Firstly, anExtended Kalman Filter is designed as a model-based approach. Then a NeuralNetwork is designed as a data-driven approach. Finally, a hybrid estimator isproposed by performing a fusion between the previous designed estimators:model-based and data-driven. All approaches consider only Global PositioningSystem (GPS), Inertial Measurement Unit (IMU) and a one dimensional Pitot tubeas available sensors. Simulations in a realistic nonlinear model of the airshipsuggest that the cooperation between these two techniques increases theestimation performance.

Hybrid Model-Based and Data-Driven Wind Velocity Estimator for an Autonomous Robotic Airship