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| Presentation | 2022-07-15 14:20 [Invited Talk] Application of state-space models to CNN estimation methods for indoor location estimation Kaishin Hori, Satoru Aikawa, Sinichiro Yamamoto (Univ. of Hyogo)  CS2022-36 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Currently, GNSS is the most accurate outdoor location estimation technique. On the other hand, the accuracy of GNSS location estimation indoors is reduced due to the poor reception of satellite signals. Therefore, research is being conducted to achieve highly accurate indoor localization by using wireless LAN. In this study, we employ Convolutional Neural Network (CNN) estimation based on the Fingerprint method for indoor WLAN positioning, which is more accurate than other methods of WLAN information. On the other hand, CNN estimation is time-independent. Therefore, a filter was used to correct for this time-series dependence. As a result, the mean estimation error was improved by up to 0.38 m. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | indoor localization / fingerprint / CNN / state space model / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 110, CS2022-36, pp. 100-103, July 2022. | |
| Paper # | CS2022-36 | |
| Date of Issue | 2022-07-07 (CS) | |
| 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 | CS2022-36 |
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| Conference Information | |
| Committee | CS |
| Conference Date | 2022-07-14 - 2022-07-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Yakushima Environmental and Cultural Village Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. |
| Paper Information | |
| Registration To | CS |
| Conference Code | 2022-07-CS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Application of state-space models to CNN estimation methods for indoor location estimation |
| Sub Title (in English) | |
| Keyword(1) | indoor localization |
| Keyword(2) | fingerprint |
| Keyword(3) | CNN |
| Keyword(4) | state space model |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Kaishin Hori |
| 1st Author's Affiliation | University of Hyogo (Univ. of Hyogo) |
| 2nd Author's Name | Satoru Aikawa |
| 2nd Author's Affiliation | University of Hyogo (Univ. of Hyogo) |
| 3rd Author's Name | Sinichiro Yamamoto |
| 3rd Author's Affiliation | University of Hyogo (Univ. of Hyogo) |
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| Speaker | Author-1 |
| Date Time | 2022-07-15 14:20:00 |
| Presentation Time | 30 minutes |
| Registration for | CS |
| Paper # | CS2022-36 |
| Volume (vol) | vol.122 |
| Number (no) | no.110 |
| Page | pp.100-103 |
| #Pages | 4 |
| Date of Issue | 2022-07-07 (CS) |
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