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| Paper Abstract and Keywords | ||
| Presentation | 2018-07-11 15:25 A Fundamental Study on Direction of Arrival Estimation with Deep Learning Yuya Kase, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa (Hokkaido Univ.)  RCC2018-33 NS2018-46 RCS2018-88 SR2018-27 ASN2018-27 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Direction of arrival (DOA) estimation of radio waves is applicable to localization of wireless terminals and radar systems. In addition to MUSIC and ESPRIT, which are well-known traditional algorithms, compressed sensing has been applied to DOA estimation. Compressed sensing has a higher accuracy compared with MUSIC in general but requires larger computational load. If such a large computational load is acceptable, we can consider to apply deep learning to DOA estimation. In this paper, we propose an estimation method using deep learning, especially with an additional network designed for closely located sources, and examine its basic characteristics in the case where two narrow-band signals impinge on a linear array. The simulation results show that high estimation accuracy is obtained and that the performance depends on the learning data sets. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | DOA estimation / deep learning / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 125, RCS2018-88, pp. 51-55, July 2018. | |
| Paper # | RCS2018-88 | |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) | |
| ISSN | Print edition: ISSN 0913-5685 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 | RCC2018-33 NS2018-46 RCS2018-88 SR2018-27 ASN2018-27 |
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| Conference Information | |
| Committee | ASN NS RCS SR RCC |
| Conference Date | 2018-07-11 - 2018-07-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hakodate Arena |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT（Internet of Things）, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2018-07-ASN-NS-RCS-SR-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Fundamental Study on Direction of Arrival Estimation with Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | DOA estimation |
| Keyword(2) | deep learning |
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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.) |
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| Speaker | Author-1 |
| Date Time | 2018-07-11 15:25:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCC2018-33, NS2018-46, RCS2018-88, SR2018-27, ASN2018-27 |
| Volume (vol) | vol.118 |
| Number (no) | no.123(RCC), no.124(NS), no.125(RCS), no.126(SR), no.127(ASN) |
| Page | pp.51-55(RCC), pp.57-61(NS), pp.51-55(RCS), pp.39-43(SR), pp.67-71(ASN) |
| #Pages | 5 |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) |
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