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基于阵列天线和稀疏贝叶斯学习的室内定位方法

刘坤 吴建新 甄杰 王彤

刘坤, 吴建新, 甄杰, 王彤. 基于阵列天线和稀疏贝叶斯学习的室内定位方法[J]. 电子与信息学报, 2020, 42(5): 1158-1164. doi: 10.11999/JEIT190314
引用本文: 刘坤, 吴建新, 甄杰, 王彤. 基于阵列天线和稀疏贝叶斯学习的室内定位方法[J]. 电子与信息学报, 2020, 42(5): 1158-1164. doi: 10.11999/JEIT190314
Kun LIU, Jianxin WU, Jie ZHEN, Tong WANG. Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1158-1164. doi: 10.11999/JEIT190314
Citation: Kun LIU, Jianxin WU, Jie ZHEN, Tong WANG. Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1158-1164. doi: 10.11999/JEIT190314

基于阵列天线和稀疏贝叶斯学习的室内定位方法

doi: 10.11999/JEIT190314
基金项目: 国家重点研发计划课题(2016YFB0502201)
详细信息
    作者简介:

    刘坤:男,1995年生,博士生,研究方向为阵列信号处理、雷达信号处理

    吴建新:男,1982年生,副教授,研究方向为阵列信号处理,雷达信号处理等

    甄杰:女,1969年生,研究员,研究方向为室内外一体化定位与导航

    王彤:男,1974年生,教授,研究方向为阵列信号处理、空时信号处理、自适应信号处理

    通讯作者:

    吴建新 jxwu@mail.xidian.edu.cn

  • 中图分类号: TN911.7; TN926.1

Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning

Funds: The National Key Research and Development Plan (2016YFB0502201)
  • 摘要:

    由于多径和非同源等因素的影响,传统基于蓝牙信号强度的室内定位方法的性能精度和稳定性都不高。针对基于蓝牙信号的复杂室内环境定位问题,该文提出基于低成本阵列天线的室内定位方法,该方法利用单通道轮采极化敏感阵列天线对蓝牙信号进行采样,然后结合暗室测量获得的准确阵列流形和极化快收敛稀疏贝叶斯学习(P-FCSBL)算法实现信源的角度估计,最后通过角度实现定位。该方法充分利用极化信息和角度信息来实现目标和多径信号的分离,同时对单信源的同时采样保证了估计的稳定性。最后通过实测数据处理验证了该方法的有效性。

  • 图  1  蓝牙阵列模型图

    图  2  插值后水平极化阵列流形及垂直极化阵列流形幅度图

    图  3  信号模型图

    图  4  阵元个数不同时定位轨迹图

    图  5  信源轨迹追踪图

    图  6  距离均方根误差图

     初始化:
      $ {\left( {{\sigma ^2}} \right)^{\left( 1 \right)}}{\rm{ = }}{\widehat \sigma ^2}$
      $ {{{v}}_i} = {{{S}}_i}{{{h}}_i},i = 1,2, ··· ,P$
      $ {{D}} = \left[ {{{{v}}_1}\;{{{v}}_2}\; ··· \;{{{v}}_P}} \right]$
      $ \gamma _i^{\left( 1 \right)} = \left| {{{v}}_i^{\rm{H}}{{x}}} \right|/\left| {{{v}}_i^{\rm{H}}{{{v}}_i}} \right|,i = 1,2, ··· ,P$
      $ {{{C}}^{\left( 1 \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( 1 \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( 1 \right)}}{{I}}} $
     迭代:
      $ {\mu ^{\left( j \right)}} = {{{\varGamma}} ^{\left( j \right)}}{{{D}}^{\rm{H}}}{\left( {{{{M}}^{\left( j \right)}}} \right)^{ - 1}}{{x}}$
      $ {\left( {{\sigma ^2}} \right)^{\left( {j + 1} \right)}} = \left( {1/N} \right)\left[ {\left\| {{{x}} - {{D}}{{{\mu}} ^{\left( j \right)}}} \right\|_2^2 + {{\left( {{\sigma ^2}} \right)}^{\left( j \right)}}\displaystyle\sum\limits_1^P {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{{v}}_i}} } \right]$
      $ \gamma _i^{\left( {j + 1} \right)} = {\left| {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{x}}} \right|^2},i = 1,2, ··· ,P$
      $ {{{C}}^{\left( {j + 1} \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( {j + 1} \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( {j + 1} \right)}}{{I}}} $
     直到得到一个满足要求的稀疏解。
    下载: 导出CSV

    表  1  两次实验定位误差分析统计表

    实验平均定位误差(m)误差≤0.2 m的占比(%)误差≤0.4 m的占比(%))误差≤1 m的占比(%)
    轨迹1 RSS1.085803.4836.32
    轨迹2 RSS1.019603.9840.80
    轨迹1 FCSBL0.124787.16100100
    轨迹2 FCSBL0.184358.2998.10100
    轨迹1 P-FCSBL0.093090.79100100
    轨迹2 P-FCSBL0.099093.23100100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-05
  • 修回日期:  2019-09-28
  • 网络出版日期:  2020-01-21
  • 刊出日期:  2020-06-04

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