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Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering

机译:基于改进卡尔曼滤波的具有NLOS识别和缓解的移动定位

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摘要

In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications.
机译:为了提高混合视线(LOS)和非视线(NLOS)环境中无线定位的准确性和可靠性,提出了一种健壮的移动定位算法来跟踪移动节点的位置( MN)。利用在更新阶段修改的扩展卡尔曼滤波器(EKF)可以减少在粗糙的无线环境中的NLOS误差,在粗糙的无线环境中,通过约束优化方法直接估算每个测量范围中包含的NLOS偏差。为了识别NLOS和LOS之间信道状况的变化,提出了一种基于创新矢量的低复杂度识别方法。数值结果表明,在不同的LOS / NLOS条件下,该算法的定位误差均明显小于迭代NLOS EKF算法和传统EKF算法的定位误差。而且,这种定位方法不需要任何NLOS错误的统计分布知识。此外,复杂性实验表明该算法支持实时应用。

著录项

  • 作者

    Ke, Wei; Wu, Lenan;

  • 作者单位
  • 年度 2011
  • 总页数
  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
  • 中图分类

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