LCRT: A ToA Based Mobile Terminal Localization Algorithm in NLOS Environment

Non line-of-sight (NLOS) propagation in range measurement is a key problem for mobile terminal localization. This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational complexity compared with...

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Hauptverfasser: Lei, Jiao, Li, Frank Yong, Zengyou, Xu
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description Non line-of-sight (NLOS) propagation in range measurement is a key problem for mobile terminal localization. This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational complexity compared with the residual test (RT) algorithm. LCRT is based on the assumption that when all range measurements are from LOS propagations, the normalized residual distribution follows the central chi-square distribution while for NLOS cases it is non-central. An optimized procedure to generate the sets of range measurements is adopted and least square (LS) instead of approximate maximum likelihood (AML) is used during the identification of LOS propagations, resulting in reduced computation complexity. Simulation results show that the LCRT can efficiently identify the set of LOS. The correct decision rate is higher than 92% and the variances of results are approaching to the Cramer-Rao lower bound (CRLB) when there are more than 3 LOS propagations.
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This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational complexity compared with the residual test (RT) algorithm. LCRT is based on the assumption that when all range measurements are from LOS propagations, the normalized residual distribution follows the central chi-square distribution while for NLOS cases it is non-central. An optimized procedure to generate the sets of range measurements is adopted and least square (LS) instead of approximate maximum likelihood (AML) is used during the identification of LOS propagations, resulting in reduced computation complexity. Simulation results show that the LCRT can efficiently identify the set of LOS. 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This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational complexity compared with the residual test (RT) algorithm. LCRT is based on the assumption that when all range measurements are from LOS propagations, the normalized residual distribution follows the central chi-square distribution while for NLOS cases it is non-central. An optimized procedure to generate the sets of range measurements is adopted and least square (LS) instead of approximate maximum likelihood (AML) is used during the identification of LOS propagations, resulting in reduced computation complexity. Simulation results show that the LCRT can efficiently identify the set of LOS. 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This paper proposes a low computational residual test (LCRT) algorithm that can identify the number of line-of-sight (LOS) transmissions and reduce the computational complexity compared with the residual test (RT) algorithm. LCRT is based on the assumption that when all range measurements are from LOS propagations, the normalized residual distribution follows the central chi-square distribution while for NLOS cases it is non-central. An optimized procedure to generate the sets of range measurements is adopted and least square (LS) instead of approximate maximum likelihood (AML) is used during the identification of LOS propagations, resulting in reduced computation complexity. Simulation results show that the LCRT can efficiently identify the set of LOS. 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