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Characterizing aircraft wake vortex position and strength using LiDAR measurements processed with artificial neural networks

Wartha, Niklas Louis and Stephan, Anton and Holzäpfel, Frank and Rotshteyn, Grigory (2022) Characterizing aircraft wake vortex position and strength using LiDAR measurements processed with artificial neural networks. Optics Express, 30 (8), pp. 13197-13225. Optical Society of America. doi: 10.1364/OE.454525. ISSN 1094-4087.

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Official URL: https://opg.optica.org/oe/fulltext.cfm?uri=oe-30-8-13197&id=471171

Abstract

The position and strength of wake vortices captured by LiDAR (Light Detection and Ranging) instruments are usually determined by conventional approaches such as the Radial Velocity (RV) method. Promising wake vortex detection results of LiDAR measurements using machine learning and operational drawbacks of the comparatively slow traditional processing methods motivate exploring the suitability of Artificial Neural Networks (ANNs) for quantitatively estimating the position and strength of aircraft wake vortices. The ANNs are trained by a unique data set of wake vortices generated by aircraft during final approach, which are labeled using the RV method. First comparisons reveal the potential of custom Convolutional Neural Networks in comparison to readily available resources as well as traditional LiDAR processing algorithms.

Item URL in elib:https://elib.dlr.de/186049/
Document Type:Article
Title:Characterizing aircraft wake vortex position and strength using LiDAR measurements processed with artificial neural networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Wartha, Niklas LouisDLR, IPAhttps://orcid.org/0000-0002-9672-2360UNSPECIFIED
Stephan, AntonDLR, IPAUNSPECIFIEDUNSPECIFIED
Holzäpfel, FrankDLR, IPAhttps://orcid.org/0000-0003-3182-1832UNSPECIFIED
Rotshteyn, GrigoryDLR, IPAUNSPECIFIEDUNSPECIFIED
Date:5 April 2022
Journal or Publication Title:Optics Express
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:30
DOI:10.1364/OE.454525
Page Range:pp. 13197-13225
Publisher:Optical Society of America
ISSN:1094-4087
Status:Published
Keywords:Wake Vortex, LiDAR, Machine Learning, Artificial Neural Networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Climate, Weather and Environment
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Wartha, Niklas Louis
Deposited On:06 Apr 2022 14:22
Last Modified:06 Apr 2022 14:22

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