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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
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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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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)  
Keyword(1) Indoor Localization  
Keyword(2) Wi-Fi  
Keyword(3) BLE  
Keyword(4) Fingerprint  
Keyword(5) 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  
4th Author's Affiliation 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
Date of Issue 2020-02-26 (RCS) 


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