MS Location Estimation Based on the Artificial Bee Colony Algorithm

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm...
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Autor*in:	Chien-Sheng Chen [verfasserIn] Jen-Fa Huang [verfasserIn] Nan-Chun Huang [verfasserIn] Kai-Sheng Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS)

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 20(2020), 19, p 5597
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:19, p 5597

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s20195597

Katalog-ID:	DOAJ085327301

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allfields	10.3390/s20195597 doi (DE-627)DOAJ085327301 (DE-599)DOAJ2a1a72c8f5184d4d985f621ed8926486 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut MS Location Estimation Based on the Artificial Bee Colony Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology Jen-Fa Huang verfasserin aut Nan-Chun Huang verfasserin aut Kai-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 20(2020), 19, p 5597 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:19, p 5597 https://doi.org/10.3390/s20195597 kostenfrei https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 kostenfrei https://www.mdpi.com/1424-8220/20/19/5597 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 20 2020 19, p 5597
spelling	10.3390/s20195597 doi (DE-627)DOAJ085327301 (DE-599)DOAJ2a1a72c8f5184d4d985f621ed8926486 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut MS Location Estimation Based on the Artificial Bee Colony Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology Jen-Fa Huang verfasserin aut Nan-Chun Huang verfasserin aut Kai-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 20(2020), 19, p 5597 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:19, p 5597 https://doi.org/10.3390/s20195597 kostenfrei https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 kostenfrei https://www.mdpi.com/1424-8220/20/19/5597 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 20 2020 19, p 5597
allfields_unstemmed	10.3390/s20195597 doi (DE-627)DOAJ085327301 (DE-599)DOAJ2a1a72c8f5184d4d985f621ed8926486 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut MS Location Estimation Based on the Artificial Bee Colony Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology Jen-Fa Huang verfasserin aut Nan-Chun Huang verfasserin aut Kai-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 20(2020), 19, p 5597 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:19, p 5597 https://doi.org/10.3390/s20195597 kostenfrei https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 kostenfrei https://www.mdpi.com/1424-8220/20/19/5597 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 20 2020 19, p 5597
allfieldsGer	10.3390/s20195597 doi (DE-627)DOAJ085327301 (DE-599)DOAJ2a1a72c8f5184d4d985f621ed8926486 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut MS Location Estimation Based on the Artificial Bee Colony Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology Jen-Fa Huang verfasserin aut Nan-Chun Huang verfasserin aut Kai-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 20(2020), 19, p 5597 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:19, p 5597 https://doi.org/10.3390/s20195597 kostenfrei https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 kostenfrei https://www.mdpi.com/1424-8220/20/19/5597 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 20 2020 19, p 5597
allfieldsSound	10.3390/s20195597 doi (DE-627)DOAJ085327301 (DE-599)DOAJ2a1a72c8f5184d4d985f621ed8926486 DE-627 ger DE-627 rakwb eng TP1-1185 Chien-Sheng Chen verfasserin aut MS Location Estimation Based on the Artificial Bee Colony Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology Jen-Fa Huang verfasserin aut Nan-Chun Huang verfasserin aut Kai-Sheng Chen verfasserin aut In Sensors MDPI AG, 2003 20(2020), 19, p 5597 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:19, p 5597 https://doi.org/10.3390/s20195597 kostenfrei https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 kostenfrei https://www.mdpi.com/1424-8220/20/19/5597 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 20 2020 19, p 5597
language	English
source	In Sensors 20(2020), 19, p 5597 volume:20 year:2020 number:19, p 5597
sourceStr	In Sensors 20(2020), 19, p 5597 volume:20 year:2020 number:19, p 5597
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topic_facet	time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS) Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Chien-Sheng Chen @@aut@@ Jen-Fa Huang @@aut@@ Nan-Chun Huang @@aut@@ Kai-Sheng Chen @@aut@@
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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 non-line-of-sight (NLOS) misc artificial bee colony (ABC) misc mobile station (MS) misc base station (BS) misc Chemical technology MS Location Estimation Based on the Artificial Bee Colony Algorithm
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topic_title	TP1-1185 MS Location Estimation Based on the Artificial Bee Colony Algorithm time of arrival (TOA) non-line-of-sight (NLOS) artificial bee colony (ABC) mobile station (MS) base station (BS)
topic	misc TP1-1185 misc time of arrival (TOA) misc non-line-of-sight (NLOS) misc artificial bee colony (ABC) misc mobile station (MS) misc base station (BS) misc Chemical technology
topic_unstemmed	misc TP1-1185 misc time of arrival (TOA) misc non-line-of-sight (NLOS) misc artificial bee colony (ABC) misc mobile station (MS) misc base station (BS) misc Chemical technology
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title	MS Location Estimation Based on the Artificial Bee Colony Algorithm
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title_full	MS Location Estimation Based on the Artificial Bee Colony Algorithm
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title_sort	ms location estimation based on the artificial bee colony algorithm
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title_auth	MS Location Estimation Based on the Artificial Bee Colony Algorithm
abstract	With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.
abstractGer	With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.
abstract_unstemmed	With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.
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title_short	MS Location Estimation Based on the Artificial Bee Colony Algorithm
url	https://doi.org/10.3390/s20195597 https://doaj.org/article/2a1a72c8f5184d4d985f621ed8926486 https://www.mdpi.com/1424-8220/20/19/5597 https://doaj.org/toc/1424-8220
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