Artificial Neural Network for Location Estimation in Wireless Communication Systems

In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information t...
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Autor*in:	Chien-Sheng Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	time of arrival (TOA) angle of arrival (AOA) non-line-of-sight (NLOS) artificial neural networks (ANN)

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 12(2012), 3, Seite 2798-2817
Übergeordnetes Werk:	volume:12 ; year:2012 ; number:3 ; pages:2798-2817

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s120302798

Katalog-ID:	DOAJ025312685

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spelling	10.3390/s120302798 doi (DE-627)DOAJ025312685 (DE-599)DOAJc953f23f83fe47b582ed6c302643b8c9 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut Artificial Neural Network for Location Estimation in Wireless Communication Systems 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments. time of arrival (TOA) angle of arrival (AOA) non-line-of-sight (NLOS) artificial neural networks (ANN) Chemical technology In Sensors MDPI AG, 2003 12(2012), 3, Seite 2798-2817 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:12 year:2012 number:3 pages:2798-2817 https://doi.org/10.3390/s120302798 kostenfrei https://doaj.org/article/c953f23f83fe47b582ed6c302643b8c9 kostenfrei http://www.mdpi.com/1424-8220/12/3/2798/ kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 12 2012 3 2798-2817
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callnumber-first	T - Technology
author	Chien-Sheng Chen
spellingShingle	Chien-Sheng Chen misc TP1-1185 misc time of arrival (TOA) misc angle of arrival (AOA) misc non-line-of-sight (NLOS) misc artificial neural networks (ANN) misc Chemical technology Artificial Neural Network for Location Estimation in Wireless Communication Systems
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topic_title	TP1-1185 Artificial Neural Network for Location Estimation in Wireless Communication Systems time of arrival (TOA) angle of arrival (AOA) non-line-of-sight (NLOS) artificial neural networks (ANN)
topic	misc TP1-1185 misc time of arrival (TOA) misc angle of arrival (AOA) misc non-line-of-sight (NLOS) misc artificial neural networks (ANN) misc Chemical technology
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title	Artificial Neural Network for Location Estimation in Wireless Communication Systems
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title_full	Artificial Neural Network for Location Estimation in Wireless Communication Systems
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title_sort	artificial neural network for location estimation in wireless communication systems
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title_auth	Artificial Neural Network for Location Estimation in Wireless Communication Systems
abstract	In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.
abstractGer	In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.
abstract_unstemmed	In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.
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title_short	Artificial Neural Network for Location Estimation in Wireless Communication Systems
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Artificial Neural Network for Location Estimation in Wireless Communication Systems

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