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Paper Abstract and Keywords
Presentation 2024-05-17 14:05
A study on indoor drone positioning using Wi-Fi RTT and machine learning
Yuichiro Sugiyama, Kentaro Kobayashi (Meijo Univ.) WBS2024-13 RCC2024-13
Abstract (in Japanese) (See Japanese page) 
(in English) When drones fly autonomously outdoors, they use GPS to implement safety functions and navigation. However, GPS is unstable and cannot provide satisfactorily these functions indoors. We have investigated Wi-Fi RTT-based indoor drone positioning methods. In this paper, we propose a method to estimate the position coordinate of a drone using machine learning. The proposed method uses Wi-Fi RTT ranging results measured in advance as training data to estimate the position coordinates of the stationary or flying drone. We compare and evaluate the estimation results obtained by the machine learning-based method with those obtained by the minimum mean square error-based method. The measurement results show that the machine learning-based estimation achieves more accurate results than the minimum mean square error-based estimation.
Keyword (in Japanese) (See Japanese page) 
(in English) Wi-Fi RTT / Drone / Ranging / Indoor Positioning / Self-localization / Machine learning / DNN /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 25, RCC2024-13, pp. 60-65, May 2024.
Paper # RCC2024-13 
Date of Issue 2024-05-09 (WBS, RCC) 
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 WBS2024-13 RCC2024-13

Conference Information
Committee SAT MICT WBS RCC  
Conference Date 2024-05-16 - 2024-05-17 
Place (in Japanese) (See Japanese page) 
Place (in English) KITEN Convention hall (Miyazaki) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Coding, Modulation, Signal processing, Reliable Communication and Control, Healthcare and Medical Information Communication Technologies, and General 
Paper Information
Registration To RCC 
Conference Code 2024-05-SAT-MICT-WBS-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on indoor drone positioning using Wi-Fi RTT and machine learning 
Sub Title (in English)  
Keyword(1) Wi-Fi RTT  
Keyword(2) Drone  
Keyword(3) Ranging  
Keyword(4) Indoor Positioning  
Keyword(5) Self-localization  
Keyword(6) Machine learning  
Keyword(7) DNN  
Keyword(8)  
1st Author's Name Yuichiro Sugiyama  
1st Author's Affiliation Meijo University (Meijo Univ.)
2nd Author's Name Kentaro Kobayashi  
2nd Author's Affiliation Meijo University (Meijo Univ.)
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Speaker Author-1 
Date Time 2024-05-17 14:05:00 
Presentation Time 20 minutes 
Registration for RCC 
Paper # WBS2024-13, RCC2024-13 
Volume (vol) vol.124 
Number (no) no.24(WBS), no.25(RCC) 
Page pp.60-65 
#Pages 6 
Date of Issue 2024-05-09 (WBS, RCC) 

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