Paper Abstract and Keywords |
Presentation |
2024-08-27 14:15
2-Step Robust DNN Model for RSSI-based Indoor Localization Taisei Kosaka (Yokohama National Univ.), Steven Wandale (Univ. of Malawi), Koichi Ichige (Yokohama National Univ.) SIP2024-60 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we introduce a novel approach called the 2-Step Robust Deep Neural Network (DNN), designed specifically for indoor localization utilizing received signal strength indicator (RSSI) data.
The conventional method, 2-Step Extreme Gradient Boosting (XGBoost), improves estimation accuracy by utilizing one of the two-dimensional coordinates ($x$ or $y$ coordinate) as an input feature. However, it has limitations in estimation accuracy for complex and nonlinear datasets, and its performance deteriorates when the input positional coordinates deviate from the ground truth during testing.
The 2-Step Robust DNN proposed in this paper incorporates two enhancements over the 2-Step XGBoost.
Through comprehensive simulations, we demonstrate that the proposed 2-Step Robust DNN attains superior estimation accuracy while preserving the absence of constraints on the dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Indoor localization / DNN / RSSI / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 124, no. 162, SIP2024-60, pp. 75-80, Aug. 2024. |
Paper # |
SIP2024-60 |
Date of Issue |
2024-08-19 (SIP) |
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) |
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SIP2024-60 |
Conference Information |
Committee |
SIP |
Conference Date |
2024-08-26 - 2024-08-27 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
University of Fukui (Bunkyo Campus) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Signal processing, etc. |
Paper Information |
Registration To |
SIP |
Conference Code |
2024-08-SIP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
2-Step Robust DNN Model for RSSI-based Indoor Localization |
Sub Title (in English) |
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Keyword(1) |
Indoor localization |
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DNN |
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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 |
2024-08-27 14:15:00 |
Presentation Time |
20 minutes |
Registration for |
SIP |
Paper # |
SIP2024-60 |
Volume (vol) |
vol.124 |
Number (no) |
no.162 |
Page |
pp.75-80 |
#Pages |
6 |
Date of Issue |
2024-08-19 (SIP) |