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Paper Abstract and Keywords
Presentation 2026-01-22 16:25
Comparative Analysis of LSTM and RNN for UWB Indoor Localization under LOS/NLOS Conditions
jin Kitsukawa, Nao Ito, Kazuya Unjo (Toyama KOSEN) CAS2025-64 ICTSSL2025-42
Abstract (in Japanese) (See Japanese page) 
(in English) In machine-learning-based indoor positioning using ultra-wideband (UWB), Long Short-Term Memory (LSTM) networks are commonly employed. However, in non-line-of-sight (NLOS) environments where obstacles blocking the radio propagation path exist between the transmitter and the receiver, UWB ranging errors caused by these obstacles increase, and there is a risk that LSTM networks retain such error components in their internal states for a long time, which can degrade positioning accuracy. In contrast, a simple recurrent neural network (RNN) without gating structures tends to forget past inputs within a short time due to the vanishing gradient problem, which is expected to reduce the influence of such noise compared with the LSTM. This study therefore evaluates and compares the positioning accuracy of an RNN and an LSTM in both line-of-sight (LOS) and NLOS environments, where obstacles are absent and present between the transmitter and receiver, respectively. Statistical analysis showed that, in the LOS environment, the LSTM achieved significantly higher positioning accuracy than the RNN, whereas in the NLOS environment, the RNN achieved significantly higher positioning accuracy than the LSTM. These results indicate that, in UWB-based indoor positioning, it is important to appropriately select the model according to the measurement environment, and this study provides experimental findings to support such selection.
Keyword (in Japanese) (See Japanese page) 
(in English) RNN / LSTM / UWB / Indoor Localization / LOS/NLOS condition / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 321, CAS2025-64, pp. 64-69, Jan. 2026.
Paper # CAS2025-64 
Date of Issue 2026-01-15 (CAS, ICTSSL) 
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 CAS2025-64 ICTSSL2025-42

Conference Information
Committee ICTSSL CAS  
Conference Date 2026-01-22 - 2026-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CAS 
Conference Code 2026-01-ICTSSL-CAS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Comparative Analysis of LSTM and RNN for UWB Indoor Localization under LOS/NLOS Conditions 
Sub Title (in English)  
Keyword(1) RNN  
Keyword(2) LSTM  
Keyword(3) UWB  
Keyword(4) Indoor Localization  
Keyword(5) LOS/NLOS condition  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name jin Kitsukawa  
1st Author's Affiliation National Institute of Technology, Toyama College (Toyama KOSEN)
2nd Author's Name Nao Ito  
2nd Author's Affiliation National Institute of Technology, Toyama College (Toyama KOSEN)
3rd Author's Name Kazuya Unjo  
3rd Author's Affiliation National Institute of Technology, Toyama College (Toyama KOSEN)
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Speaker Author-1 
Date Time 2026-01-22 16:25:00 
Presentation Time 25 minutes 
Registration for CAS 
Paper # CAS2025-64, ICTSSL2025-42 
Volume (vol) vol.125 
Number (no) no.321(CAS), no.322(ICTSSL) 
Page pp.64-69 
#Pages 6 
Date of Issue 2026-01-15 (CAS, ICTSSL) 

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