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Paper Abstract and Keywords
Presentation 2026-06-19 14:10
A Study on Data-Driven Tuning of KalmanNet for GNSS Positioning in Multipath Environments
Hayato Ishii, Shinsuke Ibi, Hisato Iwai (Doshisha Univ.) RCS2026-83
Abstract (in Japanese) (See Japanese page) 
(in English) In position estimation using the global navigation satellite system (GNSS), there is a problem where positioning accuracy significantly deteriorates in urban environments due to the influence of multipath effects from buildings and trees. As a countermeasure to this, the extended Kalman filter (EKF), which integrates kinematic models and observation values, is widely used. However, because the EKF treats the process noise covariance matrix that defines the system error as a fixed value, it is difficult to appropriately adjust the weights of observation values and predicted values against the fluctuation of noise distributions accompanying dynamic environmental changes. Therefore, this study proposes a KalmanNet utilizing two types of neural networks (NNs), multi-layer perceptron (MLP) and gated recurrent unit (GRU), to dynamically estimate and optimize the process noise covariance matrix in the EKF according to the environment. Furthermore, to verify the effectiveness of the proposed method against environmental changes over time, positioning accuracy is evaluated using training and test data acquired on different days, confirming its superiority over the conventional EKF.
Keyword (in Japanese) (See Japanese page) 
(in English) GNSS positioning / multipath / neural network / Kalman filter / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 72, RCS2026-83, pp. 325-330, June 2026.
Paper # RCS2026-83 
Date of Issue 2026-06-10 (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 RCS2026-83

Conference Information
Committee RCS  
Conference Date 2026-06-17 - 2026-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English) The Ohama Nobumoto Memorial Hall 
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 2026-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Data-Driven Tuning of KalmanNet for GNSS Positioning in Multipath Environments 
Sub Title (in English)  
Keyword(1) GNSS positioning  
Keyword(2) multipath  
Keyword(3) neural network  
Keyword(4) Kalman filter  
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Keyword(6)  
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1st Author's Name Hayato Ishii  
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 Hisato Iwai  
3rd Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2026-06-19 14:10:00 
Presentation Time 20 minutes 
Registration for RCS 
Paper # RCS2026-83 
Volume (vol) vol.126 
Number (no) no.72 
Page pp.325-330 
#Pages 6 
Date of Issue 2026-06-10 (RCS) 

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