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Conference Papers Year : 2022

Deep Learning-based Flight Speed Estimation using Thermal Anemometers

Abstract

This article concerns flight speed estimation from airflow measurements provided by a set of thermal anemometers. Our approach relies on a Gated Recurrent Unit (GRU) based deep learning approach to extract deep features from noisy and turbulent measurement signals of triaxial thermal anemometers, in order to establish the underlying mapping between the airflow measurement and the flight speed. The proposed solution is validated on a multi-rotor micro aerial vehicle (MAV). The results show that the GRUbased model can effectively extract noise features and perform denoising, and compensate for induced velocity effects along the propellers' rotation axis. As a consequence, robust prediction of the flight speed is performed, including during takeoff and landing that induce ground effects and strong variations of vertical airflow.

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Dates and versions

hal-03788445 , version 1 (26-09-2022)

Identifiers

  • HAL Id : hal-03788445 , version 1

Cite

Ze Wang, Jingang Qu, Pascal Morin. Deep Learning-based Flight Speed Estimation using Thermal Anemometers. International Micro Air Vehicle (IMAV), Sep 2022, Delft, Netherlands. ⟨hal-03788445⟩
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