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| Presentation | 2020-07-09 13:25 Considerations on Accuracy Improvement in DOA Estimation Using Deep Learning Yuya Kase, Takanori Sato, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa (Hokkaido Univ.), Daisuke Kitayama, Yoshihisa Kishiyama (NTT DOCOMO)  RCC2020-5 NS2020-34 RCS2020-68 SR2020-13 SeMI2020-5 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Direction of arrival (DOA) estimation of radio waves using a various types of array antennas are generally classified into subspace methods such as MUSIC and ESPRIT, and probability distribution estimation such as EM and SAGE. Recently, compressed sensing and deep learning have been studied with a progress of computing resources. 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, it was shown that the estimation frequently fails when a signal arrives at angles near the grid border. In this paper, we have proposed a method of combining two DNNs, of which grids are staggered, in order to reduce this estimation error. Simulation results show that the proposed combining method improves the estimation accuracy compared with the case where one DNN is used and achieves higher estimation accuracy than MUSIC. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | DOA estimation / array antenna / deep learning / deep neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 89, RCS2020-68, pp. 61-66, July 2020. | |
| Paper # | RCS2020-68 | |
| Date of Issue | 2020-07-01 (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 | RCC2020-5 NS2020-34 RCS2020-68 SR2020-13 SeMI2020-5 |
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| Conference Information | |
| Committee | SR NS SeMI RCC RCS |
| Conference Date | 2020-07-08 - 2020-07-10 |
| 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 | 2020-07-SR-NS-SeMI-RCC-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Considerations on Accuracy Improvement in DOA Estimation Using 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) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Yuya Kase |
| 1st Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 2nd Author's Name | Takanori Sato |
| 2nd Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 3rd Author's Name | Toshihiko Nishimura |
| 3rd Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name | Takeo Ohgane |
| 4th Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 5th Author's Name | Yasutaka Ogawa |
| 5th Author's Affiliation | Hokkaido University (Hokkaido Univ.) |
| 6th Author's Name | Daisuke Kitayama |
| 6th Author's Affiliation | NTT DOCOMO, INC (NTT DOCOMO) |
| 7th Author's Name | Yoshihisa Kishiyama |
| 7th Author's Affiliation | NTT DOCOMO, INC (NTT DOCOMO) |
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| Speaker | Author-1 |
| Date Time | 2020-07-09 13:25:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCC2020-5, NS2020-34, RCS2020-68, SR2020-13, SeMI2020-5 |
| Volume (vol) | vol.120 |
| Number (no) | no.87(RCC), no.88(NS), no.89(RCS), no.90(SR), no.91(SeMI) |
| Page | pp.19-24(RCC), pp.19-24(NS), pp.61-66(RCS), pp.25-30(SR), pp.13-18(SeMI) |
| #Pages | 6 |
| Date of Issue | 2020-07-01 (RCC, NS, RCS, SR, SeMI) |
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