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Paper Abstract and Keywords
Presentation 2021-01-21 13:00
Study on UWB indoor localization method using machine learning-based accurate NLOS detection
Keigo Ishida, Eiji Okamoto (NIT), Huan-Bang Li (NICT) SeMI2020-50
Abstract (in Japanese) (See Japanese page) 
(in English) According to the automatization of factory and other facilities, there is a growing demand of accurate indoor location information. We have focused on the high resolution of ultra-wideband (UWB) and its application to position estimation. One of the problems for indoor localization is the degradation of positioning accuracy due to the non line-of-sight (NLOS) environment, which is caused by the blockage of obstacles. To tackle this problem, various methods have been investigated. This paper focuses on the range-based methods which detects NLOS sensors based on the variance of multiple measurement data. Range-based methods are simple because they uses only a few metrics, while comparatively having high accuracy. However, conventional methods have problems in versatility because they used empirical thresholds. In addition, since NLOS detected sensors are removed, the performance of localization tends to degrade even if the NLOS detection is successful. Therefore, in this paper, we propose a new localization method introducing machine learning into NLOS detection to improve its versatility and accuracy. In addition, the proposed method also uses the NLOS sensors for localization by utilizing estimated true distances based on the predction function derived by training data. The performance of the proposed method is shown in comparison with other conventional methods in computer simulation. Consequently, it is shown that the proposed method is superior to other conventional methods.
Keyword (in Japanese) (See Japanese page) 
(in English) indoor localization / UWB / support vector machine / neural network / NLOS / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 315, SeMI2020-50, pp. 39-44, Jan. 2021.
Paper # SeMI2020-50 
Date of Issue 2021-01-13 (SeMI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 SeMI2020-50

Conference Information
Committee SeMI  
Conference Date 2021-01-20 - 2021-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2021-01-SeMI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on UWB indoor localization method using machine learning-based accurate NLOS detection 
Sub Title (in English)  
Keyword(1) indoor localization  
Keyword(2) UWB  
Keyword(3) support vector machine  
Keyword(4) neural network  
Keyword(5) NLOS  
1st Author's Name Keigo Ishida  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Eiji Okamoto  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Huan-Bang Li  
3rd Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Date Time 2021-01-21 13:00:00 
Presentation Time 20 
Registration for SeMI 
Paper # IEICE-SeMI2020-50 
Volume (vol) IEICE-120 
Number (no) no.315 
Page pp.39-44 
#Pages IEICE-6 
Date of Issue IEICE-SeMI-2021-01-13 

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