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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7070383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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