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

## TL;DR

This paper introduces a hybrid wind velocity estimator for autonomous airships, combining model-based and data-driven methods to improve estimation accuracy using limited sensors.

## Contribution

It proposes a novel hybrid estimator that fuses Extended Kalman Filter and Neural Network approaches for wind velocity estimation in airships.

## Key findings

- Hybrid estimator outperforms individual methods in simulations
- Fusion improves robustness and accuracy of wind estimation
- Approaches use minimal sensor data for effective estimation

## Abstract

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.