Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one n...
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Autor*in:	Jium-Ming Lin [verfasserIn] Chien-Sheng Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	time of arrival (TOA) angle of arrival (AOA) back-propagation nseural network (BPNN) resilient back-propagation (Rprop)

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 11(2011), 4, Seite 4207-4230
Übergeordnetes Werk:	volume:11 ; year:2011 ; number:4 ; pages:4207-4230

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s110404207

Katalog-ID:	DOAJ079322301

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allfields	10.3390/s110404207 doi (DE-627)DOAJ079322301 (DE-599)DOAJc410550e5d524ef4aa4893bccf72b9e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jium-Ming Lin verfasserin aut Applying Rprop Neural Network for the Prediction of the Mobile Station Location 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor. time of arrival (TOA) angle of arrival (AOA) back-propagation nseural network (BPNN) resilient back-propagation (Rprop) Chemical technology Chien-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 11(2011), 4, Seite 4207-4230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:11 year:2011 number:4 pages:4207-4230 https://doi.org/10.3390/s110404207 kostenfrei https://doaj.org/article/c410550e5d524ef4aa4893bccf72b9e9 kostenfrei http://www.mdpi.com/1424-8220/11/4/4207/ kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2011 4 4207-4230
spelling	10.3390/s110404207 doi (DE-627)DOAJ079322301 (DE-599)DOAJc410550e5d524ef4aa4893bccf72b9e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jium-Ming Lin verfasserin aut Applying Rprop Neural Network for the Prediction of the Mobile Station Location 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor. time of arrival (TOA) angle of arrival (AOA) back-propagation nseural network (BPNN) resilient back-propagation (Rprop) Chemical technology Chien-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 11(2011), 4, Seite 4207-4230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:11 year:2011 number:4 pages:4207-4230 https://doi.org/10.3390/s110404207 kostenfrei https://doaj.org/article/c410550e5d524ef4aa4893bccf72b9e9 kostenfrei http://www.mdpi.com/1424-8220/11/4/4207/ kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2011 4 4207-4230
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allfieldsSound	10.3390/s110404207 doi (DE-627)DOAJ079322301 (DE-599)DOAJc410550e5d524ef4aa4893bccf72b9e9 DE-627 ger DE-627 rakwb eng TP1-1185 Jium-Ming Lin verfasserin aut Applying Rprop Neural Network for the Prediction of the Mobile Station Location 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor. time of arrival (TOA) angle of arrival (AOA) back-propagation nseural network (BPNN) resilient back-propagation (Rprop) Chemical technology Chien-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 11(2011), 4, Seite 4207-4230 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:11 year:2011 number:4 pages:4207-4230 https://doi.org/10.3390/s110404207 kostenfrei https://doaj.org/article/c410550e5d524ef4aa4893bccf72b9e9 kostenfrei http://www.mdpi.com/1424-8220/11/4/4207/ kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2011 4 4207-4230
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callnumber-first	T - Technology
author	Jium-Ming Lin
spellingShingle	Jium-Ming Lin misc TP1-1185 misc time of arrival (TOA) misc angle of arrival (AOA) misc back-propagation nseural network (BPNN) misc resilient back-propagation (Rprop) misc Chemical technology Applying Rprop Neural Network for the Prediction of the Mobile Station Location
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topic_title	TP1-1185 Applying Rprop Neural Network for the Prediction of the Mobile Station Location time of arrival (TOA) angle of arrival (AOA) back-propagation nseural network (BPNN) resilient back-propagation (Rprop)
topic	misc TP1-1185 misc time of arrival (TOA) misc angle of arrival (AOA) misc back-propagation nseural network (BPNN) misc resilient back-propagation (Rprop) misc Chemical technology
topic_unstemmed	misc TP1-1185 misc time of arrival (TOA) misc angle of arrival (AOA) misc back-propagation nseural network (BPNN) misc resilient back-propagation (Rprop) misc Chemical technology
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title	Applying Rprop Neural Network for the Prediction of the Mobile Station Location
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title_full	Applying Rprop Neural Network for the Prediction of the Mobile Station Location
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title_sort	applying rprop neural network for the prediction of the mobile station location
callnumber	TP1-1185
title_auth	Applying Rprop Neural Network for the Prediction of the Mobile Station Location
abstract	Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor.
abstractGer	Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor.
abstract_unstemmed	Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor.
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title_short	Applying Rprop Neural Network for the Prediction of the Mobile Station Location
url	https://doi.org/10.3390/s110404207 https://doaj.org/article/c410550e5d524ef4aa4893bccf72b9e9 http://www.mdpi.com/1424-8220/11/4/4207/ https://doaj.org/toc/1424-8220
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doi_str	10.3390/s110404207
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up_date	2024-07-03T22:53:26.562Z
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