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Paper Abstract and Keywords
Presentation 2025-01-30 14:00
Data Augmentation and Selection for RSSI-Based Indoor Localization
Taisei Kosaka (Yokohama National Univ.), Steven Wandale (Univ. of Malawi), Koichi Ichige (Yokohama National Univ.) IT2024-69 SIP2024-108 RCS2024-252
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a data augmentation and selection method utilizing Generative Adversarial Networks (GANs) for indoor localization using Received Signal Strength Indicator (RSSI) to achieve high estimation accuracy with a small amount of training data.
Conventional methods have a challenge in that they perform data augmentation and selection without considering the instability typically associated with GANs training.
The proposed method addresses this by pre-training the Generator with supervised learning during the data augmentation phase and employing a data selection technique based on objective evaluation of the generated data relative to the measured data. This enables efficient augmentation of the data suitable for training the localization model.
Simulation results demonstrate that the proposed method achieves high estimation accuracy even with a small amount of RSSI data.
Keyword (in Japanese) (See Japanese page) 
(in English) Indoor localization / DNN / GAN / RSSI / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 365, SIP2024-108, pp. 250-255, Jan. 2025.
Paper # SIP2024-108 
Date of Issue 2025-01-22 (IT, SIP, 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)
Download PDF IT2024-69 SIP2024-108 RCS2024-252

Conference Information
Committee RCS SIP IT  
Conference Date 2025-01-29 - 2025-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Kaikyou-messe 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Signal Processing for Wireless Communications, Learning, Mathematics, Information Theory, etc. 
Paper Information
Registration To SIP 
Conference Code 2025-01-RCS-SIP-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data Augmentation and Selection for RSSI-Based Indoor Localization 
Sub Title (in English)  
Keyword(1) Indoor localization  
Keyword(2) DNN  
Keyword(3) GAN  
Keyword(4) RSSI  
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1st Author's Name Taisei Kosaka  
1st Author's Affiliation Yokohama National University (Yokohama National Univ.)
2nd Author's Name Steven Wandale  
2nd Author's Affiliation University of Malawi (Univ. of Malawi)
3rd Author's Name Koichi Ichige  
3rd Author's Affiliation Yokohama National University (Yokohama National Univ.)
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Speaker Author-1 
Date Time 2025-01-30 14:00:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # IT2024-69, SIP2024-108, RCS2024-252 
Volume (vol) vol.124 
Number (no) no.364(IT), no.365(SIP), no.366(RCS) 
Page pp.250-255 
#Pages 6 
Date of Issue 2025-01-22 (IT, SIP, RCS) 

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