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Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems
In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diff...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813943/ https://www.ncbi.nlm.nih.gov/pubmed/26985896 http://dx.doi.org/10.3390/s16030368 |
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author | Wan, Liangtian Han, Guangjie Wang, Hao Shu, Lei Feng, Nanxing Peng, Bao |
author_facet | Wan, Liangtian Han, Guangjie Wang, Hao Shu, Lei Feng, Nanxing Peng, Bao |
author_sort | Wan, Liangtian |
collection | PubMed |
description | In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm. |
format | Online Article Text |
id | pubmed-4813943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-48139432016-04-06 Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems Wan, Liangtian Han, Guangjie Wang, Hao Shu, Lei Feng, Nanxing Peng, Bao Sensors (Basel) Article In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm. MDPI 2016-03-12 /pmc/articles/PMC4813943/ /pubmed/26985896 http://dx.doi.org/10.3390/s16030368 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wan, Liangtian Han, Guangjie Wang, Hao Shu, Lei Feng, Nanxing Peng, Bao Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title | Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title_full | Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title_fullStr | Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title_full_unstemmed | Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title_short | Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems |
title_sort | wearable sensor localization considering mixed distributed sources in health monitoring systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813943/ https://www.ncbi.nlm.nih.gov/pubmed/26985896 http://dx.doi.org/10.3390/s16030368 |
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