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 |
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) |
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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) |
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Keyword(1) |
radar cross section |
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micro-doppler |
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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 |
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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 |
6 |
Date of Issue |
2021-10-28 (EMT) |