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Paper Abstract and Keywords
Presentation 2019-07-11 10:40
[Poster Presentation] A Study on Indoor Localization Based on Machine Learning with BLE with Compass Sensor
Kodai Tasaki, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiich Sampei (Osaka Univ.) RCC2019-29 NS2019-65 RCS2019-122 SR2019-41 SeMI2019-38
Abstract (in Japanese) (See Japanese page) 
(in English) This paper deals with an indoor localization problem based on fingerprint estimation using the received signal strength indication (RSSI) observation of BLE (Bluetooth Low Energy) beacon signals.
In fingerprint estimation, a site-survey is conducted in advance to build the radio map which can be used to match radio signatures with specific locations, thus, it can take the impacts of real-environment into consideration.
However, even if we utilize omni-directional antennas and the transmitter is on the same position, the distribution of RSSI changes due to the subtle difference in the orientation of the transmitter in real-environment.
Under such conditions, the fingerprint-based estimation accuracy is significantly degraded owing to mismatch with the map based on training data.
To solve the problem, we consider to capture the pseudo directivity of transmit antenna by using the angular information obtained from the compass sensor that is installed as standard on the multi-functional terminals in fingerpring framework.
The estimator is constructed in machine learning based multinomial logistic regression with the RSSI data set.
The experimental results show the validity of proposed position estimation algorithm.
Keyword (in Japanese) (See Japanese page) 
(in English) Indoor Localization / BLE / Fingerprint / Machine Learning / Angular Information / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 108, RCS2019-122, pp. 115-120, July 2019.
Paper # RCS2019-122 
Date of Issue 2019-07-03 (RCC, NS, RCS, SR, SeMI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
Download PDF RCC2019-29 NS2019-65 RCS2019-122 SR2019-41 SeMI2019-38

Conference Information
Committee SeMI RCS NS SR RCC  
Conference Date 2019-07-10 - 2019-07-12 
Place (in Japanese) (See Japanese page) 
Place (in English) I-Site Nanba(Osaka) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Communication and Networked Control for the Future Radio of the AI Age, etc 
Paper Information
Registration To RCS 
Conference Code 2019-07-SeMI-RCS-NS-SR-RCC 
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 Machine Learning with BLE with Compass Sensor 
Sub Title (in English)  
Keyword(1) Indoor Localization  
Keyword(2) BLE  
Keyword(3) Fingerprint  
Keyword(4) Machine Learning  
Keyword(5) Angular Information  
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1st Author's Name Kodai Tasaki  
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 Seiich Sampei  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2019-07-11 10:40:00 
Presentation Time 80 minutes 
Registration for RCS 
Paper # RCC2019-29, NS2019-65, RCS2019-122, SR2019-41, SeMI2019-38 
Volume (vol) vol.119 
Number (no) no.106(RCC), no.107(NS), no.108(RCS), no.109(SR), no.110(SeMI) 
Page pp.93-98(RCC), pp.119-124(NS), pp.115-120(RCS), pp.125-130(SR), pp.107-112(SeMI) 
#Pages
Date of Issue 2019-07-03 (RCC, NS, RCS, SR, SeMI) 


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