Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor a...

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Detalles Bibliográficos
Autores principales: Kang, SeYoung, Kim, TaeHyun, Chung, WonZoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070383/
https://www.ncbi.nlm.nih.gov/pubmed/32093207
http://dx.doi.org/10.3390/s20041159
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author Kang, SeYoung
Kim, TaeHyun
Chung, WonZoo
author_facet Kang, SeYoung
Kim, TaeHyun
Chung, WonZoo
author_sort Kang, SeYoung
collection PubMed
description We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.
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spelling pubmed-70703832020-03-19 Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks Kang, SeYoung Kim, TaeHyun Chung, WonZoo Sensors (Basel) Article We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method. MDPI 2020-02-20 /pmc/articles/PMC7070383/ /pubmed/32093207 http://dx.doi.org/10.3390/s20041159 Text en © 2020 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
Kang, SeYoung
Kim, TaeHyun
Chung, WonZoo
Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title_full Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title_fullStr Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title_full_unstemmed Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title_short Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
title_sort hybrid rss/aoa localization using approximated weighted least square in wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070383/
https://www.ncbi.nlm.nih.gov/pubmed/32093207
http://dx.doi.org/10.3390/s20041159
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