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| Presentation | 2018-06-20 13:30 A Study on Indoor Localization based on Unified Fingerprint of Wi-Fi and Bluetooth Low Energy using Machine Learning Shunsuke Tsuchida, Shinsuke Ibi, Seiivhi Sampei (Osaka Univ.)  RCS2018-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper deals with an indoor localization by fingerprint estimation with the aid of machine learning on the basis of both Wi-Fi (Wireless-Fidelity) and BLE (Bluetooth Low Energy) Received Power Strength Indication (RSSI) observations of beacon signals. Wi-Fi and BLE localizations have been individually studied so far. For estimating the indoor position, the position of the beacon transmitter can be often estimated by using three-point positioning. However, the accurate position cannot be estimated by calculating the observed RSSI in the indoor environments. This is because the principle of the three-point positioning relies on ideal free space propagation loss. Unfortunately, the RSSI does not obey the ideal case. The other approach is to consider the stochastical behavior of RSSI distribution. However, the analytical distribution does not capture the practical environments, resulting in low estimation quality. Instead of considering the accuracy of the distribution, machine learning is capable of estimating the position form RSSI data itself. In the machine learning regime, big data, which is integrating Wi-Fi and BLE RSSI, helps to improve the accuracy of the localization. This paper confirms the validity of the unified Wi-Fi and BLE localization through empirical results. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine Learning / Wi-Fi / BLE / Fingerprint / Indoor Localization / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 101, RCS2018-35, pp. 1-6, June 2018. | |
| Paper # | RCS2018-35 | |
| Date of Issue | 2018-06-13 (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 | RCS2018-35 |
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| Conference Information | |
| Committee | RCS |
| Conference Date | 2018-06-20 - 2018-06-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagasaki University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | First Presentation in IEICE Technical Committee, Railroad Communications, Inter-Vehicle Communications, Road to Vehicle Communications, Resource Control, Scheduling, Wireless Communication Systems, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2018-06-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Indoor Localization based on Unified Fingerprint of Wi-Fi and Bluetooth Low Energy using Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | Machine Learning |
| Keyword(2) | Wi-Fi |
| Keyword(3) | BLE |
| Keyword(4) | Fingerprint |
| Keyword(5) | Indoor Localization |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shunsuke Tsuchida |
| 1st Author's Affiliation | Osaka University (Osaka Univ.) |
| 2nd Author's Name | Shinsuke Ibi |
| 2nd Author's Affiliation | Osaka University (Osaka Univ.) |
| 3rd Author's Name | Seiivhi Sampei |
| 3rd Author's Affiliation | Osaka University (Osaka Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-06-20 13:30:00 |
| Presentation Time | 10 minutes |
| Registration for | RCS |
| Paper # | RCS2018-35 |
| Volume (vol) | vol.118 |
| Number (no) | no.101 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2018-06-13 (RCS) |
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