Paper Abstract and Keywords |
Presentation |
2020-03-05 10:35
A Study on Indoor Localization for a Multiuser Environment based on Unified Fingerprint of Wi-Fi and Bluetooth Low Energy using Machine Learning Shunsuke Tsuchida, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) RCS2019-353 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, services using geo-location information of smartphones and tablets have been expanding.Although GNSS (Global Navigation Satellite System) is generally utilized for outdoor localization, the estimation accuracy is significantly deteriorated in indoor NLOS (Non-Line Of Site) environments.As a typical indoor localization method, a method based on fingerprints using Received Signal Strength Indicator (RSSI) of Wi-Fi has been widely studied. In this case, the localization accuracy can be improved by installing multiple receivers as observation points and applying appropriate statistical processing to observation values.In this paper, we aim to improve the accuracy of machine learning-assisted localization using large-scale observations by using BLE (Bluetooth Low Energy), in addition to Wi-Fi.Furthermore, observations in multiple user environments are also performed, and the effect of discriminating each individual is confirmed. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Indoor Localization / Wi-Fi / BLE / Fingerprint / Machine Learning / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 448, RCS2019-353, pp. 177-182, March 2020. |
Paper # |
RCS2019-353 |
Date of Issue |
2020-02-26 (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) |
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RCS2019-353 |
Conference Information |
Committee |
RCS SR SRW |
Conference Date |
2020-03-04 - 2020-03-06 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tokyo Institute of Technology |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Mobile Communication Workshop |
Paper Information |
Registration To |
RCS |
Conference Code |
2020-03-RCS-SR-SRW |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Indoor Localization for a Multiuser Environment based on Unified Fingerprint of Wi-Fi and Bluetooth Low Energy using Machine Learning |
Sub Title (in English) |
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Indoor Localization |
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Wi-Fi |
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BLE |
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Fingerprint |
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Machine Learning |
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1st Author's Name |
Shunsuke Tsuchida |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Takumi Takahashi |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Shinsuke Ibi |
3rd Author's Affiliation |
Doshisha University (Doshisha Univ.) |
4th Author's Name |
Seiichi Sampei |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-03-05 10:35:00 |
Presentation Time |
20 minutes |
Registration for |
RCS |
Paper # |
RCS2019-353 |
Volume (vol) |
vol.119 |
Number (no) |
no.448 |
Page |
pp.177-182 |
#Pages |
6 |
Date of Issue |
2020-02-26 (RCS) |
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