Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this...

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Main Authors: Yi Gan, Xunchao Cong, Yimao Sun
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/2/390
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spelling doaj-a3e3b7b28fef44f9afb5111b30ea7a6b2020-11-25T02:20:24ZengMDPI AGSensors1424-82202020-01-0120239010.3390/s20020390s20020390Refinement of TOA Localization with Sensor Position Uncertainty in Closed-FormYi Gan0Xunchao Cong1Yimao Sun2The 10th Research Institute of CETC, Chengdu 610036, ChinaThe 10th Research Institute of CETC, Chengdu 610036, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.https://www.mdpi.com/1424-8220/20/2/390source localizationtime of arrival (toa)small sensor networksensor position uncertaintyclosed-formerror refined
collection DOAJ
language English
format Article
sources DOAJ
author Yi Gan
Xunchao Cong
Yimao Sun
spellingShingle Yi Gan
Xunchao Cong
Yimao Sun
Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
Sensors
source localization
time of arrival (toa)
small sensor network
sensor position uncertainty
closed-form
error refined
author_facet Yi Gan
Xunchao Cong
Yimao Sun
author_sort Yi Gan
title Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
title_short Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
title_full Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
title_fullStr Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
title_full_unstemmed Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
title_sort refinement of toa localization with sensor position uncertainty in closed-form
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-01-01
description The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.
topic source localization
time of arrival (toa)
small sensor network
sensor position uncertainty
closed-form
error refined
url https://www.mdpi.com/1424-8220/20/2/390
work_keys_str_mv AT yigan refinementoftoalocalizationwithsensorpositionuncertaintyinclosedform
AT xunchaocong refinementoftoalocalizationwithsensorpositionuncertaintyinclosedform
AT yimaosun refinementoftoalocalizationwithsensorpositionuncertaintyinclosedform
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