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基于几何约束及迭代的NLOS环境定位算法

邓平 谢雪

邓平, 谢雪. 基于几何约束及迭代的NLOS环境定位算法[J]. 西南交通大学学报, 2021, 56(3): 666-672. doi: 10.3969/j.issn.0258-2724.20200094
引用本文: 邓平, 谢雪. 基于几何约束及迭代的NLOS环境定位算法[J]. 西南交通大学学报, 2021, 56(3): 666-672. doi: 10.3969/j.issn.0258-2724.20200094
DENG Ping, XIE Xue. An NLOS Environment Location Algorithm Based on Geometric Constraint and Iteration[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 666-672. doi: 10.3969/j.issn.0258-2724.20200094
Citation: DENG Ping, XIE Xue. An NLOS Environment Location Algorithm Based on Geometric Constraint and Iteration[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 666-672. doi: 10.3969/j.issn.0258-2724.20200094

基于几何约束及迭代的NLOS环境定位算法

doi: 10.3969/j.issn.0258-2724.20200094
基金项目: 国家自然科学基金(61871332)
详细信息
    作者简介:

    邓平(1964—),男,教授,博士,研究方向为无线网络定位技术、统计信号处理、无线传感网络等,E-mail:pdeng@swjtu.edu.cn

  • 中图分类号: TN915.9

An NLOS Environment Location Algorithm Based on Geometric Constraint and Iteration

  • 摘要: 针对在非视距 (non-line-of-sight,NLOS)环境中传统最优化定位算法抗NLOS误差能力较弱、且需要一个较准确的初始估计位置以确保算法收敛这一问题,提出一种应用在双基站场景下的基于几何约束及迭代的定位算法. 通过引入最大散射半径作为几何约束条件,以线性迭代方式进行一维全局搜索,并采用最小二乘算法获得移动台(mobile station,MS)初始估计位置,然后利用设定的阈值门限对各初始位置点进行筛选,最后通过加权平均获得MS的最终估计位置. 仿真结果表明:当散射半径为200 m时,本文算法的定位误差在200 m以下的概率能达到100%;在相同环境下,本文算法计算时间开销仅是网格搜索法的0.4%.

     

  • 图 1  基站与移动台位置关系

    Figure 1.  Location relationship between BS and MS

    图 2  基站、移动台和散射体的几何关系

    Figure 2.  Geometric relationship of BS,MS and scatterer

    图 3  MLE随散射半径的变化曲线

    Figure 3.  MLE variation with scattering radius

    图 4  圆盘散射半径为200 m累积分布函数曲线

    Figure 4.  CDF curves with scattering radius of 200 m

    图 5  MLE与有无散射半径约束的变化曲线

    Figure 5.  MLE variation with or without scattering radius constraint

    图 6  累积分布函数曲线

    Figure 6.  CDF curves with or without scattering radius constraint

    图 7  MLE随距离测量误差的变化曲线

    Figure 7.  MLE variation with distance measurement error

    图 8  MLE随角度测量误差的变化曲线

    Figure 8.  MLE variation with angle measurement error

    图 9  误差随k的变化曲线

    Figure 9.  Location error variation with k

    表  1  算法描述

    Table  1.   Algorithm description

    算法描述
    HLOP混合 TOA/AOA 算法[1]
    IPA-1约束条件 1 下的内点法[14]
    IPA-2约束条件 2 下的内点法[14]
    GSA-1约束条件 1 下的网格法[13]
    GSA-2约束条件 2 下的网格法[13]
    算法 1约束条件 1 下的基于迭代的 MS 定位算法
    算法 2约束条件 2 下的基于迭代的 MS 定位算法
    下载: 导出CSV

    表  2  算法时间开销

    Table  2.   Algorithm time cost s

    算法IPAGSA本文迭代算法
    约束条件 10.17860.13500.0059
    约束条件 20.16440.10870.0050
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-13
  • 修回日期:  2020-06-16
  • 网络出版日期:  2020-08-25
  • 刊出日期:  2021-06-15

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