The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O objetivo da localização sem fio é determinar a localização da estação móvel (MS) em um sistema de comunicações celulares sem fio. Quando os sinais são propagados através de caminhos sem linha de visada (NLOS), as medições nas estações base (BSs) contêm grandes erros que resultam em fraca detectabilidade de uma MS pelas BSs circundantes. Nessas situações, é necessário integrar todas as medições heterogêneas disponíveis para melhorar a precisão da localização. Este artigo apresenta métodos híbridos que combinam informações de tempo de chegada (TOA) em três BSs e informações de ângulo de chegada (AOA) na BS servidora para obter uma estimativa de localização para o MS. Os métodos propostos mitigam o efeito NLOS usando a soma ponderada das interseções entre três círculos TOA e a linha AOA sem exigir o a priori conhecimento das estatísticas de erro NLOS. Os resultados numéricos mostram que todos os métodos de posicionamento oferecem maior precisão de estimativa em relação àqueles que dependem de dois círculos e duas linhas. Os métodos propostos sempre alcançam melhor precisão de localização do que o algoritmo da série de Taylor (TSA) e o algoritmo de linhas híbridas de posição (HLOP), independentemente das estatísticas de erro NLOS.
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Chien-Sheng CHEN, Szu-Lin SU, Yih-Fang HUANG, "Mobile Location Estimation in Wireless Communication Systems" in IEICE TRANSACTIONS on Communications,
vol. E94-B, no. 3, pp. 690-693, March 2011, doi: 10.1587/transcom.E94.B.690.
Abstract: The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.690/_p
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@ARTICLE{e94-b_3_690,
author={Chien-Sheng CHEN, Szu-Lin SU, Yih-Fang HUANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Mobile Location Estimation in Wireless Communication Systems},
year={2011},
volume={E94-B},
number={3},
pages={690-693},
abstract={The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.},
keywords={},
doi={10.1587/transcom.E94.B.690},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - Mobile Location Estimation in Wireless Communication Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 690
EP - 693
AU - Chien-Sheng CHEN
AU - Szu-Lin SU
AU - Yih-Fang HUANG
PY - 2011
DO - 10.1587/transcom.E94.B.690
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2011
AB - The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.
ER -