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Open AccessArticle

Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft

1
Center for Applied Geoscience, Eberhard-Karls-Universität Tübingen, Hölderlinstr. 12, 72074 Tübingen, Germany
2
Deutsches Zentrum für Luft- und Raumfahrt e.V., Münchener Str. 20, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Atmosphere 2018, 9(11), 422; https://doi.org/10.3390/atmos9110422
Received: 30 August 2018 / Revised: 22 October 2018 / Accepted: 24 October 2018 / Published: 28 October 2018
(This article belongs to the Special Issue Atmospheric Measurements with Unmanned Aerial Systems (UAS))
One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly. View Full-Text
Keywords: wind speed and direction estimation algorithms; flow probes; airspeed measurement; small unmanned aircraft systems (sUAS); unmanned aerial vehicles (UAV); remotely piloted aircraft systems (RPAS) wind speed and direction estimation algorithms; flow probes; airspeed measurement; small unmanned aircraft systems (sUAS); unmanned aerial vehicles (UAV); remotely piloted aircraft systems (RPAS)
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MDPI and ACS Style

Rautenberg, A.; Graf, M.S.; Wildmann, N.; Platis, A.; Bange, J. Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft. Atmosphere 2018, 9, 422. https://doi.org/10.3390/atmos9110422

AMA Style

Rautenberg A, Graf MS, Wildmann N, Platis A, Bange J. Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft. Atmosphere. 2018; 9(11):422. https://doi.org/10.3390/atmos9110422

Chicago/Turabian Style

Rautenberg, Alexander; Graf, Martin S.; Wildmann, Norman; Platis, Andreas; Bange, Jens. 2018. "Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft" Atmosphere 9, no. 11: 422. https://doi.org/10.3390/atmos9110422

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