ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Mobile location tracking based on NLOS identification in indoor environments

  • Key Laboratory of Electromagnetic Space Information of CAS, USTC, Hefei 230027,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.10.005
  • Received Date: 10 March 2014
  • Accepted Date: 17 May 2014
  • Rev Recd Date: 17 May 2014
  • Publish Date: 30 October 2014
    Abstract

  • Abstract

    NLOS propagation is one of the key factors that affect tracking accuracy in indoor environments. An adaptive tracking algorithm was proposed to mitigate the NLOS error for indoor mobile localization. The correlation between adjacent NLOS errors in time was analyzed and exploited. A modified extended Kalman filter (MEKF) was presented which includes the NLOS error as part of the state variables. NLOS identification was achieved based on the state estimation of MEKF. MEKF and NLOS identification were combined to implement the adaptive tracking algorithm. Simulation results demonstrate that the proposed algorithm has better tracking accuracy and adaptability in indoor environments.

    Abstract

    NLOS propagation is one of the key factors that affect tracking accuracy in indoor environments. An adaptive tracking algorithm was proposed to mitigate the NLOS error for indoor mobile localization. The correlation between adjacent NLOS errors in time was analyzed and exploited. A modified extended Kalman filter (MEKF) was presented which includes the NLOS error as part of the state variables. NLOS identification was achieved based on the state estimation of MEKF. MEKF and NLOS identification were combined to implement the adaptive tracking algorithm. Simulation results demonstrate that the proposed algorithm has better tracking accuracy and adaptability in indoor environments.
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    • References(10)
  • [1]
    Pahlavan K, Li X R, Makela J P. Indoor geolocation science and technology[J]. IEEE Communications Magazine, 2002, 40(2): 112-118.
    [2]
    常戎, 吕善伟, 王鹏宇. TOA数据处理对NLOS环境下定位精度的改善[J]. 电波科学学报, 2007, 22(1): 12-15.
    [3]
    Al-Jazzar S, Jr J C, You H R. A scattering model based approach to NLOS mitigation in TOA location systems[C]// Proceedings of 55th Vehicular Technology Conference. Birmingham, USA: IEEE Press, 2002: 861-865.
    [4]
    Wylie M P, Holtzman J. The non-line of sight problem in mobile location estimation[C]// Proceedings of the IEEE Universal Personal Communications. Cambridge, USA: IEEE Press, 1996: 827-831.
    [5]
    Wang X, Wang Z X, ODea B. A TOA-based location algorithm reducing the errors due to NLOS propagation[J]. IEEE Transactions on Vehicular Technology, 2003, 52(1): 112-116.
    [6]
    Venkatraman S, Caffery J, You H R. A novel TOA location algorithm using Los range estimation for NLOS environments[J]. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1 515-1 523.
    [7]
    Huerta J M, Vidal J, Giremus A, et al. Joint particle filter and UKF position tracking in severe non line of sight situations[J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(5): 874-888.
    [8]
    Njar M, Vidal J. Kalman tracking for mobile location in NLOS situations[C]// Proceedings of 14th IEEE Personal, Indoor and Mobile Radio Communications. Beijing, China: IEEE Press, 2003: 2 203-2 207.
    [9]
    Wang G Y, Amin M G, Zhang Y. New approach for target locations in the presence of wall ambiguities[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 301-315.
    [10]
    Jr Caffery J J. A new approach to the geometry of TOA location[C]// Proceedings of IEEE Vehicular Technology Conference. Boston, USA: IEEE Press, 2000: 1 943-1 949.
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    [1]
    Pahlavan K, Li X R, Makela J P. Indoor geolocation science and technology[J]. IEEE Communications Magazine, 2002, 40(2): 112-118.
    [2]
    常戎, 吕善伟, 王鹏宇. TOA数据处理对NLOS环境下定位精度的改善[J]. 电波科学学报, 2007, 22(1): 12-15.
    [3]
    Al-Jazzar S, Jr J C, You H R. A scattering model based approach to NLOS mitigation in TOA location systems[C]// Proceedings of 55th Vehicular Technology Conference. Birmingham, USA: IEEE Press, 2002: 861-865.
    [4]
    Wylie M P, Holtzman J. The non-line of sight problem in mobile location estimation[C]// Proceedings of the IEEE Universal Personal Communications. Cambridge, USA: IEEE Press, 1996: 827-831.
    [5]
    Wang X, Wang Z X, ODea B. A TOA-based location algorithm reducing the errors due to NLOS propagation[J]. IEEE Transactions on Vehicular Technology, 2003, 52(1): 112-116.
    [6]
    Venkatraman S, Caffery J, You H R. A novel TOA location algorithm using Los range estimation for NLOS environments[J]. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1 515-1 523.
    [7]
    Huerta J M, Vidal J, Giremus A, et al. Joint particle filter and UKF position tracking in severe non line of sight situations[J]. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(5): 874-888.
    [8]
    Njar M, Vidal J. Kalman tracking for mobile location in NLOS situations[C]// Proceedings of 14th IEEE Personal, Indoor and Mobile Radio Communications. Beijing, China: IEEE Press, 2003: 2 203-2 207.
    [9]
    Wang G Y, Amin M G, Zhang Y. New approach for target locations in the presence of wall ambiguities[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 301-315.
    [10]
    Jr Caffery J J. A new approach to the geometry of TOA location[C]// Proceedings of IEEE Vehicular Technology Conference. Boston, USA: IEEE Press, 2000: 1 943-1 949.
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