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Paper Abstract and Keywords
Presentation 2026-06-17 16:15
[Special Talk] Drone Detection and Classification Technique Using Radar Imaging with Millimeter-Wave Radar
Kenshi Ogawa, Ryohei Nakamura (NDA) SANE2026-29
Abstract (in Japanese) (See Japanese page) 
(in English) With the development of drone technology, concerns have been raised regarding the potential application of drones in terrorism and other crimes. Accordingly, a drone detection system that can classify incoming drones is needed to contain potential drone threats. We have conducted research on drone detection and classification technique using radar imaging. This paper introduces a drone classification technique by generating high-resolution inverse synthetic aperture radar (ISAR) images, and then training convolutional neural networks (CNN), a type of deep learning, with generated ISAR images. Two experimental cases were investigated using five types of drones (3DR Solo, DJI Phantom 3, DJI Mavic Pro, Parrot Anafi, DJI Mavic Mini) to demonstrate the effectiveness of our proposed method. In case 1, we tested drones moving in linear uniform motion on an electronic slider under ideal conditions in the laboratory and generated the ISAR images of the drones. The models of five drones could be classified with high accuracy by learning the features of the ISAR images. In case 2, we classified the flying drones by fine-tuning pre-trained CNN models in Case 1 with generated ISAR imagery. Notably, its classification accuracy was comparable to that of Case 1. The experimental results indicated that ISAR imagery features are valid for drone classification.
Keyword (in Japanese) (See Japanese page) 
(in English) Millimeter-Wave Radar / Radar Imaging / Inverse Synthetic Aperture Radar / Deep Learning / Drone / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 71, SANE2026-29, pp. 132-132, June 2026.
Paper # SANE2026-29 
Date of Issue 2026-06-10 (SANE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SANE2026-29

Conference Information
Committee SANE  
Conference Date 2026-06-17 - 2026-06-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Information Technology R&D Center, MITSUBISHI Electric Corp. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Radar, Tracking, EW Technologies and general 
Paper Information
Registration To SANE 
Conference Code 2026-06-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Drone Detection and Classification Technique Using Radar Imaging with Millimeter-Wave Radar 
Sub Title (in English)  
Keyword(1) Millimeter-Wave Radar  
Keyword(2) Radar Imaging  
Keyword(3) Inverse Synthetic Aperture Radar  
Keyword(4) Deep Learning  
Keyword(5) Drone  
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1st Author's Name Kenshi Ogawa  
1st Author's Affiliation National Defense Academy of Japan (NDA)
2nd Author's Name Ryohei Nakamura  
2nd Author's Affiliation National Defense Academy of Japan (NDA)
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Speaker Author-1 
Date Time 2026-06-17 16:15:00 
Presentation Time 30 minutes 
Registration for SANE 
Paper # SANE2026-29 
Volume (vol) vol.126 
Number (no) no.71 
Page p.132 
#Pages 1 
Date of Issue 2026-06-10 (SANE) 

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