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A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures

Nasr Alla, George Samuell Aiad Saleip and Maurer, Paulina and Hassan, Abdelmonem and Frangenberg, Michael and Granig, Wolfgang (2021) A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures. In: International Conference on Robotics and Artificial Intelligence (ICRAI 2021). International Conference for Robotics and Artificial Intelligence (ICRAI 2021), 19.-22. Nov. 2021, Guangzhou, China. ISBN 978-172817395-5. ISSN 1050-4729. (In Press)

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Abstract

In this work, it has been proven that radar systems in general can be applied for detecting multi-copters using Deep Learning techniques. The radar micro-Doppler signatures induced due to micro movements inside the target (the rotating propellers) can be used to detect flying multi-copters. For higher classification accuracy and better reliability, a new technology applying Deep Learning can therefore be developed to detect multi-copters merely based on their radar micro-Doppler images. For the training purposes, a huge amount of data is needed. Building a new dataset however is really time consuming, which can be considered as one of the main downsides of Deep Learning. Also, powerful hardware (multiple GPUs) and much time are needed during the training process of a neural network. Data acquisition algorithm has been developed for the extraction of micro-Doppler signatures from the collected raw radar data. The micro-Doppler images were then labelled and fed to the neural network model for the purpose of applying supervised learning. After training the neural network model, the model was tested for classification accuracy to show a recognition accuracy of 99.4% dataset can therefore be applied for use in combination with different short-range radar sensor systems. Using the model, multi-copters up to a range of about 1.5m from the radar system will be detected.

Item URL in elib:https://elib.dlr.de/146854/
Document Type:Conference or Workshop Item (Speech)
Title:A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Nasr Alla, George Samuell Aiad SaleipGeorge.NasrAlla (at) dlr.deUNSPECIFIED
Maurer, PaulinaPaulina.Maurer (at) dlr.deUNSPECIFIED
Hassan, AbdelmonemAbdelmonem.Hassan (at) bmw.deUNSPECIFIED
Frangenberg, MichaelMichael.Frangenberg (at) ifr.uni-stuttgart.deUNSPECIFIED
Granig, WolfgangWolfgang.Granig (at) infineon.comUNSPECIFIED
Date:2021
Journal or Publication Title:International Conference on Robotics and Artificial Intelligence (ICRAI 2021)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
ISSN:1050-4729
ISBN:978-172817395-5
Status:In Press
Keywords:Conventional Neural Network (CNN), Micro-Doppler Signatures, Radar Cross Section (RCS), Short-time Fourier Transform (STFT)
Event Title:International Conference for Robotics and Artificial Intelligence (ICRAI 2021)
Event Location:Guangzhou, China
Event Type:international Conference
Event Dates:19.-22. Nov. 2021
Organizer:Guangzhou University of Technology: School of Automation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Tasks SISTEC
Location: Braunschweig
Institutes and Institutions:Institute for Software Technology > Software for Space Systems and Interactive Visualisation
Institute for Software Technology
Deposited By: Nasr Alla, George Samuell Aiad Saleip
Deposited On:22 Dec 2021 10:21
Last Modified:22 Dec 2021 10:21

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