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基于迁移学习提高WiFi室内定位中信道状态信息指纹库的鲁棒性

李玉柏 孙迅

李玉柏, 孙迅. 基于迁移学习提高WiFi室内定位中信道状态信息指纹库的鲁棒性[J]. 电子与信息学报, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160
引用本文: 李玉柏, 孙迅. 基于迁移学习提高WiFi室内定位中信道状态信息指纹库的鲁棒性[J]. 电子与信息学报, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160
LI Yubai, SUN Xun. A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160
Citation: LI Yubai, SUN Xun. A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3657-3666. doi: 10.11999/JEIT221160

基于迁移学习提高WiFi室内定位中信道状态信息指纹库的鲁棒性

doi: 10.11999/JEIT221160
基金项目: 四川省重点研发计划(23ZDYF0198)
详细信息
    作者简介:

    李玉柏:男,教授,研究方向为软件无线电、移动通信与信号处理

    孙迅:男,硕士生,研究方向为室内定位与信号处理

    通讯作者:

    孙迅 xsun@std.uestc.edu.cn

  • 中图分类号: TN92

A Highly Robust Indoor Location Algorithm Using WiFi Channel State Information Based on Transfer Learning Reinforcement

Funds: Key R & D plan of Sichuan Province (23ZDYF0198)
  • 摘要: 基于信道状态信息(CSI)数据的WiFi指纹可用于室内定位。与信号强度值(RSSI)数据相比,CSI具有更高的数据信息粒度,并且可以在多个子载波上获得。当使用CSI数据进行室内定位时,相对于RSSI可以获得更好的结果。然而,无论使用RSSI还是CSI信号,在室内定位部署期间的一段时间后,室内环境通常会发生变化,并且基于测试数据的指纹数据库通常会恶化甚至失效。该文提出使用迁移学习算法来建立用于室内定位的指纹数据库。迁移学习的优势在于,可以使用较少的数据来获得更好的迁移训练结果。该文使用迁移学习来迁移指纹数据库的预测,延长指纹数据库的生命周期,并提高室内定位的鲁棒性。经过实验,1周后室内定位准确率保持在98%,两周后保持在97%。在相同成本下,该模型的生命周期和定位精度高于长短期记忆网络(LSTM)、卷积神经网络(CNN)、支持向量机(SVM)、深度神经网络(DNN)和其他定位系统。
  • 图  1  室内环境变化前后定位误差

    图  2  室内定位系统架构

    图  3  数据预处理算法流程图

    图  4  迁移学习网络架构

    图  5  CSI数据拼接

    图  6  室内环境

    图  7  CSI数据预处理算法比较

    图  8  对比RSSI与各维度下的CSI数据

    图  9  室内定位中的不同方法

    算法1 数据预处理算法
     输入:${\bf{CSI} }_{i}$, ${\rm{RS}}\mathrm{S}{\mathrm{I} }_{i}$
     输出:预处理后的${\bf{CSI} }_{i}$
     (1) for ${\bf{CSI} }_{i}$ do
     (2)   for ${\bf{CSI} }_{ij}$ do
     (3)     通过式(5)计算$ s; $
     (4)     通过式(6)修正${\bf{CSI} }_{i}$;
     (5)   end
     (6)   if ${\bf{CSI} }_{i}$ 不是最大的聚类部分 then
     (7)     删除${\bf{CSI} }_{i}$;
     (8)   end
     (9)   删除非数据子载波;
     (10)   利用PCA算法进行降维;
     (11)  end
    下载: 导出CSV
    算法2 迁移学习训练与位置预测
     输入:经过数据预处理后的训练数据${ {\bf{CSI} } } _{i}^{S}$,测试数据${\bf{CSI}}_{i}^{T}$
     输出:训练好的指纹库,预测位置
     (1) for ${{\bf{CSI}}}_{i}^{S}$ do
     (2)   输入到特征提取器以获得$ {Z}_{i}^{S}; $
     (3)   通过式(10)计算${y}_{i}^{\left(S;{M}_{{\rm{P}}}\right)}$;
     (4)   通过式(11)计算$ {L}_{\mathrm{a}} $;
     (5)   计算域间损失$ {L}_{\mathrm{d}} $;
     (6)   反向传播更新网络参数;
     (7) end
     (8) for ${\bf{CSI}} _{i}^{T}$ do
     (9)   输入到特征提取器以获得${{\boldsymbol{Z}}}_{i}^{T}$;
     (10)   通过式(10)计算${y}_{i}^{\left(T;{M}_{{\rm{P}}}\right)}$;
     (11)   输出位置预测结果。
     (12) end
    下载: 导出CSV
    算法3 网络更新算法
     输入:测试数据${\bf{C}\bf{S}{\bf{I} }}_{i}^{ {\rm{t} } }$,信标数据${\bf{C}\bf{S}{\bf{I} }}_{i}^{{\rm{b}}}$
     输出:更新后的网络
     (1) for ${\bf{C}\bf{S}{\bf{I} }}_{i}^{{\rm{t}}}$ do
     (2)   执行算法1:数据预处理算法;
     (3)   if $D\left(\bf{C}\bf{S}{\bf{I} }_{i}\right) < \mathrm{\delta }$且无异常值then
     (4)     作为训练数据执行算法2:迁移学习训练算法;
     (5)   end
     (6) end
     (7) for ${\bf{C}\bf{S}{\bf{I} }}_{i}^{{\rm{b}}}$ do
     (8)   执行算法1:数据预处理算法;
     (9)   作为训练数据执行算法2:迁移学习训练算法;
     (10) end
    下载: 导出CSV

    表  1  数据降维带来的精度提升(%)

    CSI_pre帧过滤提升
    第1周995247
    第2周984553
    第3周973265
    数据降维AGC补偿提升
    第1周944846
    第2周843747
    第3周762848
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-06
  • 修回日期:  2023-02-14
  • 网络出版日期:  2023-02-19
  • 刊出日期:  2023-10-31

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