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基于改进免疫粒子群优化算法的室内可见光通信三维定位方法

陈勇 郑瀚 沈奇翔 刘焕淋

陈勇, 郑瀚, 沈奇翔, 刘焕淋. 基于改进免疫粒子群优化算法的室内可见光通信三维定位方法[J]. 电子与信息学报, 2021, 43(1): 101-107. doi: 10.11999/JEIT190936
引用本文: 陈勇, 郑瀚, 沈奇翔, 刘焕淋. 基于改进免疫粒子群优化算法的室内可见光通信三维定位方法[J]. 电子与信息学报, 2021, 43(1): 101-107. doi: 10.11999/JEIT190936
Yong CHEN, Han ZHENG, Qixiang SHEN, Huanlin LIU. Indoor Three-dimensional Positioning System Based on Visible Light Communication Using Improved Immune PSO Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(1): 101-107. doi: 10.11999/JEIT190936
Citation: Yong CHEN, Han ZHENG, Qixiang SHEN, Huanlin LIU. Indoor Three-dimensional Positioning System Based on Visible Light Communication Using Improved Immune PSO Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(1): 101-107. doi: 10.11999/JEIT190936

基于改进免疫粒子群优化算法的室内可见光通信三维定位方法

doi: 10.11999/JEIT190936
基金项目: 国家自然科学基金(51977021),重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0613)
详细信息
    作者简介:

    陈勇:男,1963年生,博士,教授,研究方向为可见光通信

    郑瀚:男,1996年生,硕士生,研究方向为室内可见光通信定位与导航

    沈奇翔:男,1995年生,硕士,研究方向为室内可见光通信定位

    刘焕淋:女,1970年生,博士,教授,主要研究方向为光纤通信及信号处理等

    通讯作者:

    陈勇 chenyong@cqupt.edu.cn

  • 中图分类号: TN929.1

Indoor Three-dimensional Positioning System Based on Visible Light Communication Using Improved Immune PSO Algorithm

Funds: The National Natural Science Foundation of China (51977021), The Chongqing Natural Science Foundation (cstc2019jcyj-msxmX0613)
  • 摘要:

    针对室内可见光通信中3维定位精度不高和定位时间较长的问题,该文提出基于改进免疫粒子群(IIMPSO)算法的室内可见光通信(VLC) 3维定位方法。通过分析室内多径效应,选取合适的视场角(FOV)以减少反射影响,同时完善了倾斜状态下的定位模型,并采用卡尔曼滤波算法以降低环境干扰对接收功率的影响,在此基础上与改进的免疫粒子群算法相融合。仿真结果表明,在5 m×5 m×3 m的室内环境中,该文所提出的3维定位系统平均定位误差为0.031 m,定位时长为2.3 s。与现有的3维定位系统进行比较,其定位精度与收敛速度均得到明显改善。

  • 图  1  室内可见光通信模型

    图  2  倾斜状态下可见光通信模型示意图

    图  3  接收平面总接收功率分布图

    图  4  接收平面接收到直射功率分布图

    图  5  接收平面接收到非直射功率分布图

    图  6  基于卡尔曼滤波算法的接收功率滤波优化图

    图  7  基于IIMPSO算法的室内可见光3维定位流程图

    图  8  位于中心A处的粒子迭代分布及适应度收敛曲线图

    图  9  接收端高度为0.5 m下定位误差图及误差柱状图

    图  10  接收端高度为1.5 m下定位误差图及误差柱状图

    表  1  免疫粒子群与3维定位问题的映射关系

    免疫算法粒子群算法3维定位问题
    抗原适应度函数定位误差和约束条件
    抗体粒子可行的定位位置
    抗原的识别粒子的适应度值评估3维定位问题的分析
    亲和度适应度值可行解的匹配程度
    细胞活化粒子选择选择高质量的可行解
    记忆细胞全局最优粒子定位搜索过程中的最优解
    免疫调节对当前解浓度和亲和度控制
    下载: 导出CSV

    表  2  IIMPSO算法参数表

    符号参数数值
    c1max, c2max学习因子最大值2.2
    c1min, c2min学习因子最小值0.2
    N粒子群数目30
    titer最大迭代次数100
    Pc交叉概率0.30
    Pm变异概率0.05
    vmax最大速度限制0.3
    cth粒子相似度阈值0.1
    α协调系数0.5
    fth适应度值阈值0.4
    下载: 导出CSV

    表  3  预处理效果测试(平均定位误差(m))

    状况CPSO[14]ACO[7]IIMPSO
    接收平面水平和RSS未处理0.0980.0840.054
    接收平面倾斜0.0720.0680.047
    RSS处理0.0650.0580.042
    接收平面倾斜并采用RSS处理0.0480.0430.035
    下载: 导出CSV

    表  4  IIMPSO算法参数表

    参数符号
    房间大小L×W×H5 m×5 m×3 m
    接收端高度H1 m
    发射功率Pt452 mW
    半功率角Φ1/270°
    阵列中LED的数目3×3
    LED的间隔0.01 m
    集中器增益Ts(Ψ)1
    PD的接收面积A1 cm2
    O/E 转换效率0.53 A/W
    PD的折射率n1.5
    LED中心发光强度I(0)23.81cd
    下载: 导出CSV

    表  5  不同算法有效性验证

    指标CPSO[14]ACO[7]IGA[5]SAPSO[6]IIMPSO
    算法定位时间(s)4.445.226.215.412.03
    平均定位误差(m)0.0640.0430.0560.0300.031
    最大定位误差(m)0.1550.0750.0130.0840.082
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-21
  • 修回日期:  2020-06-10
  • 网络出版日期:  2020-07-17
  • 刊出日期:  2021-01-15

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