| 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 |
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 |
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) |
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| 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 |
3 |
| Date of Issue |
2024-12-12 (ED, MWPTHz) |