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| Presentation | 2022-03-04 16:40 Denoising Method Using Deep Image Prior for Improving Accuracy of Radar Target Detection Koji Endo, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.)  RCS2021-299 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide various applications such as activity recognition and vital sign sensing. For these applications, it is essential to estimate a target’s location. In general, MIMO FMCW radar-based location estimation uses a range-angle map that expresses signal power against each range and angle. It could be possible to estimate an target’s location by detecting signal power that exceeds a threshold. However, noise and multipath components often exist over the range-angle map, which could bring a false alarm of an undesired location, depending on the threshold setting. To deal with this issue, it is highly demanded to reduce such noise components over the range-angle map. In this report, we propose Deep Image Prior (DIP)-based noise reduction method of the range-angle map. DIP is one of the unsupervised deep learning techniques that enable image denoising. In the proposed method, DIP is applied to the range-angle map calculated to reduce the noise components, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human location in indoor environments, we confirmed that the proposed method with DIP could reduce the number of false alarms and estimate the human location with less sensitivity of the threshold setting, compared to the method without DIP. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Radar / Denoising / Deep Image Prior / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 391, RCS2021-299, pp. 241-246, March 2022. | |
| Paper # | RCS2021-299 | |
| Date of Issue | 2022-02-23 (RCS) | |
| 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 | RCS2021-299 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2022-03-02 - 2022-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2022-03-RCS-SR-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Denoising Method Using Deep Image Prior for Improving Accuracy of Radar Target Detection |
| Sub Title (in English) | |
| Keyword(1) | Radar |
| Keyword(2) | Denoising |
| Keyword(3) | Deep Image Prior |
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| 1st Author's Name | Koji Endo |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Kohei Yamamoto |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-03-04 16:40:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2021-299 |
| Volume (vol) | vol.121 |
| Number (no) | no.391 |
| Page | pp.241-246 |
| #Pages | 6 |
| Date of Issue | 2022-02-23 (RCS) |
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