Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification

Radio tomographic imaging (RTI) is a technique for estimating spatial loss fields (SLFs), which maps the quantified attenuation of radio signals at every spatial location within monitored regions. In this study, we investigate RTI techniques in indoor factory environments, where the RTI techniques d...

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Main Authors: Takahiro Matsuda, Yoshiaki Nishikawa, Eiji Takahashi, Takeo Onishi, Toshiki Takeuchi
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10058967/
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author Takahiro Matsuda
Yoshiaki Nishikawa
Eiji Takahashi
Takeo Onishi
Toshiki Takeuchi
author_facet Takahiro Matsuda
Yoshiaki Nishikawa
Eiji Takahashi
Takeo Onishi
Toshiki Takeuchi
author_sort Takahiro Matsuda
collection DOAJ
description Radio tomographic imaging (RTI) is a technique for estimating spatial loss fields (SLFs), which maps the quantified attenuation of radio signals at every spatial location within monitored regions. In this study, we investigate RTI techniques in indoor factory environments, where the RTI techniques deteriorate because of severe multipath channels. We propose the binary radio tomographic imaging (binary RTI) method, where the attenuation level of each pixel in a SLF is defined as a binary value. The binary RTI method is suited for factory environments, including metallic objects, because radio signals are almost fully reflected rather than getting absorbed by such objects. In the proposed method, we suppose that transmitted signals are modulated with an orthogonal frequency division multiplexing (OFDM) format, and each receiver is equipped with multiple antenna elements. By adopting the two-dimensional multiple signal classification (MUSIC), the proposed method identifies whether the signals are transmitted in a line-of-sight (LOS) or a non-line-of-sight (NLOS) path. From the LOS/NLOS identification, we propose two algorithms to estimate the binary SLF: a simple greedy algorithm and a relaxation algorithm with low-rank approximation. We evaluate the performance of the proposed method via simulation experiments. To assess the applicability of the proposed method to factory environments, we assume a severe multipath environment where all the objects, wall, and ceiling are perfect electrical conductors, and show that by using an appropriate threshold parameter for the LOS/NLOS identification, the proposed method can estimate the binary SLF in the test environment.
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spelling doaj.art-b6390e31bdda450097fb36cedb44b17d2023-03-10T00:00:32ZengIEEEIEEE Access2169-35362023-01-0111224182242910.1109/ACCESS.2023.325256810058967Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS IdentificationTakahiro Matsuda0https://orcid.org/0000-0002-9728-9968Yoshiaki Nishikawa1Eiji Takahashi2https://orcid.org/0000-0003-3402-2024Takeo Onishi3Toshiki Takeuchi4Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, JapanGraduate School of Systems Design, Tokyo Metropolitan University, Tokyo, JapanSecure Systems Platforms Research Laboratories, NEC Corporation, Kawasaki, Kanagawa, JapanSecure Systems Platforms Research Laboratories, NEC Corporation, Kawasaki, Kanagawa, JapanAdvanced Network Research Laboratories, NEC Corporation, Kawasaki, Kanagawa, JapanRadio tomographic imaging (RTI) is a technique for estimating spatial loss fields (SLFs), which maps the quantified attenuation of radio signals at every spatial location within monitored regions. In this study, we investigate RTI techniques in indoor factory environments, where the RTI techniques deteriorate because of severe multipath channels. We propose the binary radio tomographic imaging (binary RTI) method, where the attenuation level of each pixel in a SLF is defined as a binary value. The binary RTI method is suited for factory environments, including metallic objects, because radio signals are almost fully reflected rather than getting absorbed by such objects. In the proposed method, we suppose that transmitted signals are modulated with an orthogonal frequency division multiplexing (OFDM) format, and each receiver is equipped with multiple antenna elements. By adopting the two-dimensional multiple signal classification (MUSIC), the proposed method identifies whether the signals are transmitted in a line-of-sight (LOS) or a non-line-of-sight (NLOS) path. From the LOS/NLOS identification, we propose two algorithms to estimate the binary SLF: a simple greedy algorithm and a relaxation algorithm with low-rank approximation. We evaluate the performance of the proposed method via simulation experiments. To assess the applicability of the proposed method to factory environments, we assume a severe multipath environment where all the objects, wall, and ceiling are perfect electrical conductors, and show that by using an appropriate threshold parameter for the LOS/NLOS identification, the proposed method can estimate the binary SLF in the test environment.https://ieeexplore.ieee.org/document/10058967/Factory environmentLOS/NLOS identificationradio tomographic imaging
spellingShingle Takahiro Matsuda
Yoshiaki Nishikawa
Eiji Takahashi
Takeo Onishi
Toshiki Takeuchi
Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
IEEE Access
Factory environment
LOS/NLOS identification
radio tomographic imaging
title Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
title_full Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
title_fullStr Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
title_full_unstemmed Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
title_short Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification
title_sort binary radio tomographic imaging in factory environments based on los nlos identification
topic Factory environment
LOS/NLOS identification
radio tomographic imaging
url https://ieeexplore.ieee.org/document/10058967/
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