Estimating wind fields using drones in a network

Estimating hyperlocal wind fields with on-board sensors on quadcopters

Master Thesis (2023)
Author(s)

E.B. van Baasbank (TU Delft - Aerospace Engineering)

Contributor(s)

J. Hoekstra – Coach (TU Delft - Control & Simulation)

Junzi Sun – Mentor (TU Delft - Control & Simulation)

Emmanuel Sunil – Graduation committee member (Royal Netherlands Aerospace Centre NLR)

Faculty
Aerospace Engineering
Copyright
© 2023 Erik van Baasbank
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Erik van Baasbank
Graduation Date
10-02-2023
Awarding Institution
Delft University of Technology
Project
METSIS
Programme
Aerospace Engineering
Faculty
Aerospace Engineering
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Abstract

Charting hyperlocal wind using a drone is a challenge of increased attention as it unlocks potential in a variety of fields. In context of the METeo Sensors In the Sky project, this study proposes a method to estimate the magnitude and direction of wind using a quadcopter in hover and cruise without a dedicated wind sensor. Only on-board sensors are used, with no knowledge of thrust and rpm. A deterministic method models drag experienced by the drone classically as a quadratic function of true airspeed, and estimates wind by deducting the estimated true airspeed with the GPS ground speed. Additionally, a particle filter is implemented and compared to the deterministic method. To validate the proposed methods, a series of verification flights is conducted in which the drone is flown straight into the wind, perpendicular to, and away from the wind. The results show that the proposed method can estimate wind for various ground speeds and altitudes. The root mean square error ranges between 0.3-2.0 m/s and 5-35 degrees in most scenarios with high true airspeeds. In most cases, the particle filter shows a slight improvement over the deterministic method, at the cost of reduced adaptivity to wind changes (gusts).

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