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Paper Abstract and Keywords
Presentation 2024-12-19 16:20
Concealed Object Classification Based on FMCW Millimeter-Wave Radar System Utilizing PointNet++
Yaheng Wang, Jie Su, Hironaru Murakami, Masayoshi Tonouchi (Osaka Univ.) ED2024-57 MWPTHz2024-69
Abstract (in Japanese) (See Japanese page) 
(in English) This study established a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) technology employing a frequency-modulated continuous wave (FMCW) millimeter-wave radar (MMW) imaging system and developed a technique for precise 2D and 3D reconstruction using observed point cloud data. By analyzing the intermediate frequency (IF) signal, range FFT spectra were obtained with information on the distance to the object and the reflection intensity. A measurement accuracy of less than 1 mm was achieved by successfully mitigating noise in smooth surface 3D point cloud data. The acquired high-precision data facilitated the application of deep learning for object classification. Utilizing PointNet++ for point cloud classification, our model demonstrated an accuracy of 0.998 on the training set and 0.996 on the test set. Moreover, the trained model accurately classified concealed objects in four-sided scans, confirming its practical efficacy.
Keyword (in Japanese) (See Japanese page) 
(in English) Millimeter wave radar / Synthetic aperture radar / Deep learning / Object classification / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 315, MWPTHz2024-69, pp. 15-17, Dec. 2024.
Paper # MWPTHz2024-69 
Date of Issue 2024-12-12 (ED, MWPTHz) 
ISSN Online edition: ISSN 2432-6380
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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 ED2024-57 MWPTHz2024-69

Conference Information
Committee ED MWPTHz  
Conference Date 2024-12-19 - 2024-12-20 
Place (in Japanese) (See Japanese page) 
Place (in English) RIEC, Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MWPTHz 
Conference Code 2024-12-ED-MWPTHz 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Concealed Object Classification Based on FMCW Millimeter-Wave Radar System Utilizing PointNet++ 
Sub Title (in English)  
Keyword(1) Millimeter wave radar  
Keyword(2) Synthetic aperture radar  
Keyword(3) Deep learning  
Keyword(4) Object classification  
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1st Author's Name Yaheng Wang  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Jie Su  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Hironaru Murakami  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Masayoshi Tonouchi  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2024-12-19 16:20:00 
Presentation Time 25 minutes 
Registration for MWPTHz 
Paper # ED2024-57, MWPTHz2024-69 
Volume (vol) vol.124 
Number (no) no.314(ED), no.315(MWPTHz) 
Page pp.15-17 
#Pages
Date of Issue 2024-12-12 (ED, MWPTHz) 


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