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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Published inSensors (Basel, Switzerland) Vol. 21; no. 5; p. 1728
Main Authors Rampa, Vittorio, Nicoli, Monica, Manno, Chiara, Savazzi, Stefano
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 03.03.2021
MDPI
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ISSN1424-8220
1424-8220
DOI10.3390/s21051728

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Abstract 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.
AbstractList 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.
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.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.
Author Nicoli, Monica
Manno, Chiara
Rampa, Vittorio
Savazzi, Stefano
AuthorAffiliation 1 Istituto di Elettronica, Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; stefano.savazzi@ieiit.cnr.it
2 Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; monica.nicoli@polimi.it
3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; chiara.manno90@gmail.com
AuthorAffiliation_xml – name: 3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; chiara.manno90@gmail.com
– name: 2 Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; monica.nicoli@polimi.it
– name: 1 Istituto di Elettronica, Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy; stefano.savazzi@ieiit.cnr.it
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  orcidid: 0000-0002-2263-8596
  surname: Rampa
  fullname: Rampa, Vittorio
– sequence: 2
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Issue 5
Keywords Bayesian methods
passive localization
device-free localization
particle filters
body models
diffraction models
multiple targets localization
Language English
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This paper is an extended version of the conference paper “Device-free Localization of Multiple Targets” of the same authors published in the Proceedings of the 24th European Signal Processing Conference (EUSIPCO’16), 2016, Budapest, 29 August–2 September 738–742.
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Snippet In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless...
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StartPage 1728
SubjectTerms Accuracy
Algorithms
Approximation
Bayesian methods
body models
device-free localization
diffraction models
Localization
Methods
multiple targets localization
passive localization
Propagation
Radio communications
Wireless networks
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Title EM Model-Based Device-Free Localization of Multiple Bodies
URI https://www.ncbi.nlm.nih.gov/pubmed/33802274
https://www.proquest.com/docview/2497919553
https://www.proquest.com/docview/2508562578
https://pubmed.ncbi.nlm.nih.gov/PMC7959124
https://doaj.org/article/5d2162a2f8af4a3b8cee0de1801b7b9b
Volume 21
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