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Paper Abstract and Keywords
Presentation 2021-06-24 13:00
A Study on Recurrent Neural Network Aided GNSS Positioning
Kohei Nishioka, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2021-53
Abstract (in Japanese) (See Japanese page) 
(in English) One method of positioning schemes with the aid of the global navigation satellite system (GNSS) is to approximately solve nonlinear simultaneous equations. However, these nonlinear simultaneous equations include external factors such as clock errors and multipath, which induce position estimation errors. To address the errors, this study focuses on the fact that the data obtained from the distance measurement signals broadcasted by positioning satellites is time-series data. Based on the focus point, two types of recurrent neural network (RNN) for time-series data are designed. The first one inputs pseudo-range and position coordinates of satellites. The other inputs solutions of successive approximation methods as pre-processing. The former relies on raw data, which makes the learning unstable depending on the availability of satellite observations. In contrast, the latter exploits on a model called the successive approximation method, resulting in stable learning at the sacrifice of computational complexity. In order to evaluate the usefulness of the RNN, a neural network consisting of fully connected layers is implemented for comparison.
Keyword (in Japanese) (See Japanese page) 
(in English) GNSS / pseudo-range / neural network / positioning / generalization performance / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 72, RCS2021-53, pp. 145-150, June 2021.
Paper # RCS2021-53 
Date of Issue 2021-06-16 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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 RCS2021-53

Conference Information
Committee RCS  
Conference Date 2021-06-23 - 2021-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2021-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Recurrent Neural Network Aided GNSS Positioning 
Sub Title (in English)  
Keyword(1) GNSS  
Keyword(2) pseudo-range  
Keyword(3) neural network  
Keyword(4) positioning  
Keyword(5) generalization performance  
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1st Author's Name Kohei Nishioka  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Shinsuke Ibi  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Takumi Takahashi  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Hisato Iwai  
4th Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker
Date Time 2021-06-24 13:00:00 
Presentation Time 10 
Registration for RCS 
Paper # IEICE-RCS2021-53 
Volume (vol) IEICE-121 
Number (no) no.72 
Page pp.145-150 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2021-06-16 


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