Print Email Facebook Twitter Estimating wind fields using drones in a network Title Estimating wind fields using drones in a network: Estimating hyperlocal wind fields with on-board sensors on quadcopters Author van Baasbank, Erik (TU Delft Aerospace Engineering) Contributor Hoekstra, J.M. (graduation committee) Sun, Junzi (mentor) Sunil, Emmanuel (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Project METSIS Date 2023-02-10 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). Subject quadcoptersstate estimationwind estimationparticle filtersensorlessimu To reference this document use: http://resolver.tudelft.nl/uuid:f184f81c-f691-4064-aea5-e844f8d2ea86 Part of collection Student theses Document type master thesis Rights © 2023 Erik van Baasbank Files PDF MSc_thesis_erikvanbaasbank.pdf 7.48 MB Close viewer /islandora/object/uuid:f184f81c-f691-4064-aea5-e844f8d2ea86/datastream/OBJ/view