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CAEFi:基于卷积自编码器降维的信道状态信息指纹室内定位方法

王旭东 刘帅 吴楠

王旭东, 刘帅, 吴楠. CAEFi:基于卷积自编码器降维的信道状态信息指纹室内定位方法[J]. 电子与信息学报, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663
引用本文: 王旭东, 刘帅, 吴楠. CAEFi:基于卷积自编码器降维的信道状态信息指纹室内定位方法[J]. 电子与信息学报, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663
WANG Xudong, LIU Shuai, WU Nan. CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663
Citation: WANG Xudong, LIU Shuai, WU Nan. CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2757-2766. doi: 10.11999/JEIT210663

CAEFi:基于卷积自编码器降维的信道状态信息指纹室内定位方法

doi: 10.11999/JEIT210663
基金项目: 国家自然基金(61371091)
详细信息
    作者简介:

    王旭东:男,1967年生,博士,教授,研究方向为MIMO无线通信、空间调制、光无线通信及定位

    刘帅:男,1996年生,硕士生,研究方向为无线室内定位

    吴楠:男,1979年生,博士,副教授,研究方向为现代移动无线通信系统、可见光通信系统(包括MIMO、OFDM、信道编码、协作通信、自组织网络)等

    通讯作者:

    吴楠 alfred.nan.wu@gmail.com

  • 中图分类号: TN929.1

CAEFI: Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction

Funds: The National Natural Science Foundation of China (61371091)
  • 摘要: 针对提高Wi-Fi指纹室内定位技术性能,该文首先提出一种基于卷积神经网络(CNN)的信道状态信息(CSI)指纹室内定位方法。该方法在离线阶段联合CSI幅度差和相位差信息对CNN模型进行训练。在廊厅和实验室两种不同室内定位场景进行了定位实验,分别获得了25 cm和48 cm的平均定位误差;然后,在此基础上重点针对提高基于CNN的CSI室内定位时效性,引入卷积自编码器(CAE)实现CSI的降维处理,在保证原始定位方法精度的前提下,定位时间提高了40%,同时将内存消耗降低到原算法的1/15,实验结果验证了所提算法的有效性。
  • 图  1  CNNFi系统结构

    图  2  CNNFi误差收敛情况

    图  3  卷积自编码网络结构

    图  4  CAEFi系统结构

    图  5  廊厅场景

    图  6  实验室场景

    图  7  廊厅误差累计分布图

    图  8  实验室误差累计分布图

    图  9  幅度与幅度差的平均定位误差

    图  10  相位差的差值对比图

    图  11  CSI降维平均定位误差

    图  12  不同维度的平均定位误差

    图  13  不同测试数据个数的平均定位误差

    表  1  CNN网络参数

    网络层参数输出维度
    输入层训练数据(30,30,3,m)
    2维卷积层1Conv 2D,fs=5,s=1,padding=same(30,30,16,m)
    2维卷积层2Conv 2D,fs=5,s=1,padding=same(30,30,16,m)
    2维卷积层3Conv 2D,fs=2,s=2,padding=valid(15,15,32,m)
    2维卷积层4Conv 2D,fs=5,s=1,padding=same(15,15,32,m)
    平坦层K=7200(7200,m)
    全连接层1K=1024(1024,m)
    全连接层2K=512(512,m)
    输出层K=Nrp(Nrp,m)
    下载: 导出CSV

    表  2  CAE网络参数

    网络层参数输出维度
    输入层训练数据(90,1,m)
    1维卷积层1Conv 1D,fs=5,s=1,padding=same(90,64,m)
    1维池化层1Maxpool 1D,s=2,padding=same(45,64,m)
    1维卷积层2Conv 1D,fs=5,s=1,padding=same(45,32,m)
    1维池化层2Maxpool 1D,s=2,padding=same(23,32,m)
    1维卷积层3Conv 1D,fs=5,s=1,padding=same(23,32,m)
    1维池化层3Maxpool 1D,s=2,padding=same(12,32,m)
    1维卷积层4Conv 1D,fs=5,s=1,padding=same(12,12,m)
    1维卷积层5Conv 1D,fs=3,s=1,padding=valid(10,10,m)
    1维上采样层1Upsampool 1D,size=2(20,10,m)
    1维卷积层6Conv 1D,fs=9,s=1,padding=valid(12,12,m)
    1维上采样层2Upsampool 1D,size=2(24,12,m)
    1维卷积层7Conv 1D,fs=2,s=1,padding=valid(23,23,m)
    1维上采样层3Upsampool 1D,size=2(46,23,m)
    1维卷积层8Conv 1D,fs=2,s=1,padding=valid(45,32,m)
    1维上采样层4Upsampool 1D,size=2(90,32,m)
    1维卷积层9Conv 1D,fs=5s=1,padding=same(90,64,m)
    输出层Conv 1D,fs=5,s=1,padding=same(90,1,m)
    下载: 导出CSV

    表  3  廊厅定位误差

    定位算法平均误差(m)标准差(m)
    CNNFi combine0.250.58
    CNNFi single0.731.24
    CiFi1.091.28
    DeepFi0.991.62
    PhaseFi1.101.53
    ImageFi0.931.34
    下载: 导出CSV

    表  4  实验室定位误差

    定位算法平均误差(m)标准差(m)
    CNNFi combine AP=20.480.86
    CNNFi combine AP=10.720.86
    CNNFi single1.151.22
    CiFi1.361.14
    DeepFi1.451.21
    PhaseFi1.711.43
    ImageFi1.671.47
    下载: 导出CSV

    表  5  3种神经网络的训练参数

    神经网络可训练参数非训练参数总参数
    CNNFi794441432647947678
    Auto Encoder16121016121
    CAE-CNN4904941344491838
    下载: 导出CSV

    表  6  两种定位方法的在线定位时间与内存大小

    定位方法Matlab运行
    时间(s)
    Python运行
    时间(s)
    总运行
    时间(s)
    内存大小
    (MB)
    CNNFi0.12690.13990.266891
    CAEFi0.00620.15980.16605.7898
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-02
  • 修回日期:  2021-10-28
  • 网络出版日期:  2021-11-10
  • 刊出日期:  2022-08-17

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