Two Supervised Machine Learning Approaches for Wind Velocity Estimation Using Multi-Rotor Copter Attitude Measurements

In this work we address the adequacy of two machine learning methods to tackle the problem of wind velocity estimation in the lowermost region of the atmosphere using on-board inertial drone data within an outdoor setting. We fed these data, and accompanying wind tower measurements, into a K-nearest...

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Hlavní autoři:	David Crowe, Raghava Pamula, Hing Yuet Cheung, Stephan F. J. De Wekker
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2020-10-01
Edice:	Sensors
Témata:	wind estimation machine learning drone attitude quadcopter long short-term memory (LSTM) k-nearest neighbors (KNN)
On-line přístup:	https://www.mdpi.com/1424-8220/20/19/5638
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