A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments
Main Author: | |
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Publication Date: | 2017 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/11144/3641 |
Summary: | This letter addresses the problem of target localization in harsh indoor environments based on range measurements. To mitigate the non-line-of-sight (NLOS) bias, we propose a novel robust estimator by transforming the localization problem into a generalized trust region sub-problem framework. Although still non-convex in general, this class of problems can be readily solved exactly by means of bisection procedure. The new approach does not require to make any assumptions about the statistics of NLOS bias, nor to try to distinguish which links are NLOS and which are not. Unlike the existing algorithms, the computational complexity of the proposed algorithm is linear in the number of reference nodes. Our simulation results corroborate the effectiveness of the new algorithm in terms of NLOS bias mitigation and show that the performance of our estimator is highly competitive with the performance of the stateof-the-art algorithms. In fact, they show that the novel estimator outperforms slightly the existing ones in general, and that it always provides a feasible solution |
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A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS EnvironmentsRobust LocalizationTime of Arrival (TOA),Non-Line-of-Sight (NLOS)Generalized Trust Region Sub-Problem (GTRS)Wireless Sensor Network (WSN).This letter addresses the problem of target localization in harsh indoor environments based on range measurements. To mitigate the non-line-of-sight (NLOS) bias, we propose a novel robust estimator by transforming the localization problem into a generalized trust region sub-problem framework. Although still non-convex in general, this class of problems can be readily solved exactly by means of bisection procedure. The new approach does not require to make any assumptions about the statistics of NLOS bias, nor to try to distinguish which links are NLOS and which are not. Unlike the existing algorithms, the computational complexity of the proposed algorithm is linear in the number of reference nodes. Our simulation results corroborate the effectiveness of the new algorithm in terms of NLOS bias mitigation and show that the performance of our estimator is highly competitive with the performance of the stateof-the-art algorithms. In fact, they show that the novel estimator outperforms slightly the existing ones in general, and that it always provides a feasible solutionIEEE2018-04-11T16:30:36Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11144/3641eng10.1109/LCOMM.2017.2737985Tomic, SlavisaBeko, MarkoDinis, RuiCarvalho, Paulo Montemuzainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-08-01T02:01:09Zoai:repositorio.ual.pt:11144/3641Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:40:20.581587Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
title |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
spellingShingle |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments Tomic, Slavisa Robust Localization Time of Arrival (TOA), Non-Line-of-Sight (NLOS) Generalized Trust Region Sub-Problem (GTRS) Wireless Sensor Network (WSN). |
title_short |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
title_full |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
title_fullStr |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
title_full_unstemmed |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
title_sort |
A Robust Bisection-based Estimator for TOA-based Target Localization in NLOS Environments |
author |
Tomic, Slavisa |
author_facet |
Tomic, Slavisa Beko, Marko Dinis, Rui Carvalho, Paulo Montemuza |
author_role |
author |
author2 |
Beko, Marko Dinis, Rui Carvalho, Paulo Montemuza |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Tomic, Slavisa Beko, Marko Dinis, Rui Carvalho, Paulo Montemuza |
dc.subject.por.fl_str_mv |
Robust Localization Time of Arrival (TOA), Non-Line-of-Sight (NLOS) Generalized Trust Region Sub-Problem (GTRS) Wireless Sensor Network (WSN). |
topic |
Robust Localization Time of Arrival (TOA), Non-Line-of-Sight (NLOS) Generalized Trust Region Sub-Problem (GTRS) Wireless Sensor Network (WSN). |
description |
This letter addresses the problem of target localization in harsh indoor environments based on range measurements. To mitigate the non-line-of-sight (NLOS) bias, we propose a novel robust estimator by transforming the localization problem into a generalized trust region sub-problem framework. Although still non-convex in general, this class of problems can be readily solved exactly by means of bisection procedure. The new approach does not require to make any assumptions about the statistics of NLOS bias, nor to try to distinguish which links are NLOS and which are not. Unlike the existing algorithms, the computational complexity of the proposed algorithm is linear in the number of reference nodes. Our simulation results corroborate the effectiveness of the new algorithm in terms of NLOS bias mitigation and show that the performance of our estimator is highly competitive with the performance of the stateof-the-art algorithms. In fact, they show that the novel estimator outperforms slightly the existing ones in general, and that it always provides a feasible solution |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-04-11T16:30:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/11144/3641 |
url |
http://hdl.handle.net/11144/3641 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/LCOMM.2017.2737985 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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