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EM Model-Based Device-Free Localization of Multiple Bodies †

In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting...

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Autores principales: Rampa, Vittorio, Nicoli, Monica, Manno, Chiara, Savazzi, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959124/
https://www.ncbi.nlm.nih.gov/pubmed/33802274
http://dx.doi.org/10.3390/s21051728
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author Rampa, Vittorio
Nicoli, Monica
Manno, Chiara
Savazzi, Stefano
author_facet Rampa, Vittorio
Nicoli, Monica
Manno, Chiara
Savazzi, Stefano
author_sort Rampa, Vittorio
collection PubMed
description In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed.
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spelling pubmed-79591242021-03-16 EM Model-Based Device-Free Localization of Multiple Bodies † Rampa, Vittorio Nicoli, Monica Manno, Chiara Savazzi, Stefano Sensors (Basel) Article In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed. MDPI 2021-03-03 /pmc/articles/PMC7959124/ /pubmed/33802274 http://dx.doi.org/10.3390/s21051728 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rampa, Vittorio
Nicoli, Monica
Manno, Chiara
Savazzi, Stefano
EM Model-Based Device-Free Localization of Multiple Bodies †
title EM Model-Based Device-Free Localization of Multiple Bodies †
title_full EM Model-Based Device-Free Localization of Multiple Bodies †
title_fullStr EM Model-Based Device-Free Localization of Multiple Bodies †
title_full_unstemmed EM Model-Based Device-Free Localization of Multiple Bodies †
title_short EM Model-Based Device-Free Localization of Multiple Bodies †
title_sort em model-based device-free localization of multiple bodies †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959124/
https://www.ncbi.nlm.nih.gov/pubmed/33802274
http://dx.doi.org/10.3390/s21051728
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