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Paper Abstract and Keywords
Presentation 2023-07-28 14:50
Improving position estimation accuracy method by reducing RSSI fluctuations in BLE fingerprinting-based indoor positioning
Jingshi Qian, Nobuyoshi Komuro (Chiba Univ.) CS2023-58
Abstract (in Japanese) (See Japanese page) 
(in English) The complex indoor environment will reflect and absorb the RSSI (Received Signal Strength Indicator) from the sensor. Because it is difficult to infer the location directly by signal strength, the position fingerprinting method is often used as an indoor positioning method. The general position fingerprinting method constructs a fingerprint database by recording the RSSI of each location in the room in advance and then compares the RSSI of the point to be located with the fingerprint database to estimate the location. However, because of the irregular indoor environment, the average value of RSSI usually has a large offset above and below the ideal decay curve. This leads to a relatively large error in the fingerprint database, which affects the accuracy of positioning. This study proposes a method to improve position estimation accuracy by reducing RSSI fluctuations for Machine Learning (ML)-based fingerprinting method. The proposed method fixes the RSSI offset caused by the environment before positioning using the BPNN (Back-Propagation Neural Network) algorithm. And for positioning stability, PF (Particle Filter) is introduced for post-processing. Experiments show that this method is effective in reducing the error by more than 10% for k-NN (k-Nearest Neighbors), BPNN, and SVR (Support Vector Regression).
Keyword (in Japanese) (See Japanese page) 
(in English) Position Fingerprinting Method / Received Signal Strength Indicator (RSSI) / Back-Propagation Neural Network (BPNN) / Bluetooth Low Energy (BLE) / Particle Filter / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 137, CS2023-58, pp. 163-168, July 2023.
Paper # CS2023-58 
Date of Issue 2023-07-20 (CS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee CS  
Conference Date 2023-07-27 - 2023-07-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Hachijo-machi Chamber of Commerce and Industry 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. 
Paper Information
Registration To CS 
Conference Code 2023-07-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving position estimation accuracy method by reducing RSSI fluctuations in BLE fingerprinting-based indoor positioning 
Sub Title (in English)  
Keyword(1) Position Fingerprinting Method  
Keyword(2) Received Signal Strength Indicator (RSSI)  
Keyword(3) Back-Propagation Neural Network (BPNN)  
Keyword(4) Bluetooth Low Energy (BLE)  
Keyword(5) Particle Filter  
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Keyword(7)  
Keyword(8)  
1st Author's Name Jingshi Qian  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Nobuyoshi Komuro  
2nd Author's Affiliation Chiba University (Chiba Univ.)
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Speaker Author-1 
Date Time 2023-07-28 14:50:00 
Presentation Time 20 minutes 
Registration for CS 
Paper # CS2023-58 
Volume (vol) vol.123 
Number (no) no.137 
Page pp.163-168 
#Pages
Date of Issue 2023-07-20 (CS) 


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