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| Presentation | 2021-07-16 14:30 Considerations on Accuracy Improvement in Close DOA Estimation with Deep Learning Yuya Kase, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Takanori Sato (Hokkaido Univ.), Yoshihisa Kishiyama (NTT DOCOMO)  RCC2021-39 NS2021-55 RCS2021-97 SR2021-39 SeMI2021-28 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In addition to subspace methods such as MUSIC and ESPRIT, recently, compressed sensing and deep learning have been applied to direction of arrival (DOA) estimation of radio waves using various types of array antennas with the progress of computing power. The compressed sensing and deep learning are on-grid estimation in general, and thus a discrete spectrum is obtained. In our previous studies on DOA estimation using deep learning, we proposed a method of combining two DNNs, of which grids are staggered, in order to reduce the estimation error occurring when a signal arrives at angles near the grid border. In this paper, we evaluate the estimation accuracy when our proposed method is applied to a close DOA scenario. In addition, we consider the case where the networks trained with and without close DOAs restriction are used in parallel. The simulation results show that the RMSE of staggered DNNs is improved by combining a network trained with close DOAs restriction, although it alone is not suitable for the close DOA case. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | DOA estimation / array antenna / deep learning / deep neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 103, RCS2021-97, pp. 98-103, July 2021. | |
| Paper # | RCS2021-97 | |
| Date of Issue | 2021-07-07 (RCC, NS, RCS, SR, SeMI) | |
| 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 | RCC2021-39 NS2021-55 RCS2021-97 SR2021-39 SeMI2021-28 |
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| Conference Information | |
| Committee | RCS SR NS SeMI RCC |
| Conference Date | 2021-07-14 - 2021-07-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Network Technology of the AI Age, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2021-07-RCS-SR-NS-SeMI-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Considerations on Accuracy Improvement in Close DOA Estimation with Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | DOA estimation |
| Keyword(2) | array antenna |
| Keyword(3) | deep learning |
| Keyword(4) | deep neural network |
| Keyword(5) | |
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| 1st Author's Name | Yuya Kase |
| 1st Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 2nd Author's Name | Toshihiko Nishimura |
| 2nd Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 3rd Author's Name | Takeo Ohgane |
| 3rd Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name | Yasutaka Ogawa |
| 4th Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 5th Author's Name | Takanori Sato |
| 5th Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 6th Author's Name | Yoshihisa Kishiyama |
| 6th Author's Affiliation | NTT DOCOMO, INC (NTT DOCOMO) |
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| Speaker | Author-1 |
| Date Time | 2021-07-16 14:30:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCC2021-39, NS2021-55, RCS2021-97, SR2021-39, SeMI2021-28 |
| Volume (vol) | vol.121 |
| Number (no) | no.101(RCC), no.102(NS), no.103(RCS), no.104(SR), no.105(SeMI) |
| Page | pp.77-82(RCC), pp.118-123(NS), pp.98-103(RCS), pp.100-105(SR), pp.76-81(SeMI) |
| #Pages | 6 |
| Date of Issue | 2021-07-07 (RCC, NS, RCS, SR, SeMI) |
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