A Deep Neural Network-Based LOS Classification for Triangulation Positioning

Chahyeon Eom; Chungyong Lee

doi:10.1109/ICAIIC48513.2020.9065061

A Deep Neural Network-Based LOS Classification for Triangulation Positioning

Chahyeon Eom, Chungyong Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We propose a deep neural network based a line-of-sight classification for triangulation positioning. The accuracy of triangulation decreases in the non-line-of-sight environments but finding the channel which has a line-of-sight path is difficult in the mathematical method. A proposed scheme can solve this problem by taking advantage of deep neural network. Performance evaluation is carried out by the system level simulator of Long Term Evolution-Advanced with Spatial Channel Model. Performance results indicate that the proposed scheme can effectively increase the positioning accuracy and robustness of the inaccurate received signal strength measurements.

Original language	English
Title of host publication	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	598-601
Number of pages	4
ISBN (Electronic)	9781728149851
DOIs	https://doi.org/10.1109/ICAIIC48513.2020.9065061
Publication status	Published - 2020 Feb
Event	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan Duration: 2020 Feb 19 → 2020 Feb 21

Publication series

Name	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Country/Territory	Japan
City	Fukuoka
Period	20/2/19 → 20/2/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2019R1A2C1010950).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Information Systems and Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Signal Processing

Access to Document

10.1109/ICAIIC48513.2020.9065061

Cite this

Eom, C., & Lee, C. (2020). A Deep Neural Network-Based LOS Classification for Triangulation Positioning. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 598-601). [9065061] (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065061

Eom, Chahyeon ; Lee, Chungyong. / A Deep Neural Network-Based LOS Classification for Triangulation Positioning. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 598-601 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

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abstract = "We propose a deep neural network based a line-of-sight classification for triangulation positioning. The accuracy of triangulation decreases in the non-line-of-sight environments but finding the channel which has a line-of-sight path is difficult in the mathematical method. A proposed scheme can solve this problem by taking advantage of deep neural network. Performance evaluation is carried out by the system level simulator of Long Term Evolution-Advanced with Spatial Channel Model. Performance results indicate that the proposed scheme can effectively increase the positioning accuracy and robustness of the inaccurate received signal strength measurements.",

author = "Chahyeon Eom and Chungyong Lee",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2019R1A2C1010950). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 ; Conference date: 19-02-2020 Through 21-02-2020",

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language = "English",

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Eom, C & Lee, C 2020, A Deep Neural Network-Based LOS Classification for Triangulation Positioning. in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020., 9065061, 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 598-601, 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, 20/2/19. https://doi.org/10.1109/ICAIIC48513.2020.9065061

A Deep Neural Network-Based LOS Classification for Triangulation Positioning. / Eom, Chahyeon; Lee, Chungyong.

2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 598-601 9065061 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2019R1A2C1010950). Publisher Copyright: © 2020 IEEE.

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Y1 - 2020/2

N2 - We propose a deep neural network based a line-of-sight classification for triangulation positioning. The accuracy of triangulation decreases in the non-line-of-sight environments but finding the channel which has a line-of-sight path is difficult in the mathematical method. A proposed scheme can solve this problem by taking advantage of deep neural network. Performance evaluation is carried out by the system level simulator of Long Term Evolution-Advanced with Spatial Channel Model. Performance results indicate that the proposed scheme can effectively increase the positioning accuracy and robustness of the inaccurate received signal strength measurements.

AB - We propose a deep neural network based a line-of-sight classification for triangulation positioning. The accuracy of triangulation decreases in the non-line-of-sight environments but finding the channel which has a line-of-sight path is difficult in the mathematical method. A proposed scheme can solve this problem by taking advantage of deep neural network. Performance evaluation is carried out by the system level simulator of Long Term Evolution-Advanced with Spatial Channel Model. Performance results indicate that the proposed scheme can effectively increase the positioning accuracy and robustness of the inaccurate received signal strength measurements.

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Eom C, Lee C. A Deep Neural Network-Based LOS Classification for Triangulation Positioning. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 598-601. 9065061. (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). doi: 10.1109/ICAIIC48513.2020.9065061

A Deep Neural Network-Based LOS Classification for Triangulation Positioning

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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