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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Detalles Bibliográficos
Autores principales: Crowe, David, Pamula, Raghava, Cheung, Hing Yuet, De Wekker, Stephan F. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Letter
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583910/
https://www.ncbi.nlm.nih.gov/pubmed/33023130
http://dx.doi.org/10.3390/s20195638

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583910/
https://www.ncbi.nlm.nih.gov/pubmed/33023130
http://dx.doi.org/10.3390/s20195638

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