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Paper Abstract and Keywords
Presentation 2021-11-04 14:15
A Radar Cross Section Analysis to Generate Micro-Doppler Signatures Simulation Data for Machine Learning
Ryotaro Ohashi, Hiroshi Suenobu, Dai Sasakawa, Michio Takikawa, Yoshio Inasawa (Mitsubishi Electric Corp.) EMT2021-32 Link to ES Tech. Rep. Archives: EMT2021-32
Abstract (in Japanese) (See Japanese page) 
(in English) We investigate the expansion of training data by simulated data for a machine learning model that identifies drones from micro-Doppler (μ-D) signatures. In order to obtain the μ-D signatures, the time characteristics of radar cross section (RCS) are needed. In this paper, we propose a simple, fast method to calculate the RCS of a scatter with rotator. In this method, the scatter is divided into moving parts and static parts, and the entire RCS of scatter is calculated by summation of the observation angle characteristics of scattered field of the moving parts and the scattered field of the static parts. The RCS and μ-D signature of a drone calculated by proposed method agree well with the full-model analysis and experimental results.
Keyword (in Japanese) (See Japanese page) 
(in English) radar cross section / micro-doppler / drone / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 226, EMT2021-32, pp. 19-24, Nov. 2021.
Paper # EMT2021-32 
Date of Issue 2021-10-28 (EMT) 
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 EMT2021-32 Link to ES Tech. Rep. Archives: EMT2021-32

Conference Information
Committee EMT IEE-EMT  
Conference Date 2021-11-04 - 2021-11-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Electromagnetic Theory, etc. 
Paper Information
Registration To EMT 
Conference Code 2021-11-EMT-EMT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Radar Cross Section Analysis to Generate Micro-Doppler Signatures Simulation Data for Machine Learning 
Sub Title (in English)  
Keyword(1) radar cross section  
Keyword(2) micro-doppler  
Keyword(3) drone  
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1st Author's Name Ryotaro Ohashi  
1st Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
2nd Author's Name Hiroshi Suenobu  
2nd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
3rd Author's Name Dai Sasakawa  
3rd Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
4th Author's Name Michio Takikawa  
4th Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
5th Author's Name Yoshio Inasawa  
5th Author's Affiliation Mitsubishi Electric Corporation (Mitsubishi Electric Corp.)
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Speaker Author-1 
Date Time 2021-11-04 14:15:00 
Presentation Time 25 minutes 
Registration for EMT 
Paper # EMT2021-32 
Volume (vol) vol.121 
Number (no) no.226 
Page pp.19-24 
#Pages
Date of Issue 2021-10-28 (EMT) 


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