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NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks
The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to add...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165043/ https://www.ncbi.nlm.nih.gov/pubmed/30205490 http://dx.doi.org/10.3390/s18092991 |
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author | Hua, Jingyu Yin, Yejia Lu, Weidang Zhang, Yu Li, Feng |
author_facet | Hua, Jingyu Yin, Yejia Lu, Weidang Zhang, Yu Li, Feng |
author_sort | Hua, Jingyu |
collection | PubMed |
description | The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs. |
format | Online Article Text |
id | pubmed-6165043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61650432018-10-10 NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks Hua, Jingyu Yin, Yejia Lu, Weidang Zhang, Yu Li, Feng Sensors (Basel) Article The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs. MDPI 2018-09-07 /pmc/articles/PMC6165043/ /pubmed/30205490 http://dx.doi.org/10.3390/s18092991 Text en © 2018 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 Hua, Jingyu Yin, Yejia Lu, Weidang Zhang, Yu Li, Feng NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title | NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title_full | NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title_fullStr | NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title_full_unstemmed | NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title_short | NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks |
title_sort | nlos identification and positioning algorithm based on localization residual in wireless sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165043/ https://www.ncbi.nlm.nih.gov/pubmed/30205490 http://dx.doi.org/10.3390/s18092991 |
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