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

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

大连海事大学信息科学技术学院大连 116000

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
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- 被引次数: 0
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

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

Information Science Technology College, Dalian Maritime University, Dalian 116000, China

Funds: The National Natural Science Foundation of China (61371091)

摘要

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。
- 室内指纹定位 /
- 信道状态信息 /
- 卷积神经网络 /
- 卷积自编码器
Abstract: In order to improve the performance of Wi-Fi fingerprint indoor positioning technology, a method based on Convolutional Neural Networks (CNN) for Channel State Information (CSI) fingerprint indoor positioning is first proposed. This method combines the CSI amplitude difference and phase difference information to train the CNN model in the offline stage. Positioning experiments are carried out in two different indoor positioning scenarios in the gallery and the laboratory, and the average positioning errors of 25 cm and 48 cm are obtained respectively; Then, on this basis, the focus is on improving the timeliness of CNN-based CSI indoor positioning. The Convolutional AutoEncoder (CAE) is introduced to realize the dimensionality reduction processing of CSI. Under the premise of ensuring the accuracy of the original positioning method, the positioning time is increased by 40% and the memory consumption is reduced to 1/15 of the original algorithm. The experimental results verify the effectiveness of the proposed algorithm.
- Indoor fingerprint positioning /
- Channel State Information(CSI) /
- Convolutional Neural Network(CNN) /
- Convolutional AutoEncoder(CAE)

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图 1 CNNFi系统结构

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图 2 CNNFi误差收敛情况

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图 3 卷积自编码网络结构

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图 4 CAEFi系统结构

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图 5 廊厅场景

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图 6 实验室场景

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图 7 廊厅误差累计分布图

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图 8 实验室误差累计分布图

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图 9 幅度与幅度差的平均定位误差

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图 10 相位差的差值对比图

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图 11 CSI降维平均定位误差

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图 12 不同维度的平均定位误差

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图 13 不同测试数据个数的平均定位误差

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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1	Conv 2D,f_s=5,s=1,padding=same	(30,30,16,m)
2维卷积层2	Conv 2D,f_s=5,s=1,padding=same	(30,30,16,m)
2维卷积层3	Conv 2D,f_s=2,s=2,padding=valid	(15,15,32,m)
2维卷积层4	Conv 2D,f_s=5,s=1,padding=same	(15,15,32,m)
平坦层	K=7200	(7200,m)
全连接层1	K=1024	(1024,m)
全连接层2	K=512	(512,m)
输出层	K=N_rp	(N_rp,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1	Conv 1D,f_s=5,s=1,padding=same	(90,64,m)
1维池化层1	Maxpool 1D,s=2,padding=same	(45,64,m)
1维卷积层2	Conv 1D,f_s=5,s=1,padding=same	(45,32,m)
1维池化层2	Maxpool 1D,s=2,padding=same	(23,32,m)
1维卷积层3	Conv 1D,f_s=5,s=1,padding=same	(23,32,m)
1维池化层3	Maxpool 1D,s=2,padding=same	(12,32,m)
1维卷积层4	Conv 1D,f_s=5,s=1,padding=same	(12,12,m)
1维卷积层5	Conv 1D,f_s=3,s=1,padding=valid	(10,10,m)
1维上采样层1	Upsampool 1D,size=2	(20,10,m)
1维卷积层6	Conv 1D,f_s=9,s=1,padding=valid	(12,12,m)
1维上采样层2	Upsampool 1D,size=2	(24,12,m)
1维卷积层7	Conv 1D,f_s=2,s=1,padding=valid	(23,23,m)
1维上采样层3	Upsampool 1D,size=2	(46,23,m)
1维卷积层8	Conv 1D,f_s=2,s=1,padding=valid	(45,32,m)
1维上采样层4	Upsampool 1D,size=2	(90,32,m)
1维卷积层9	Conv 1D,f_s=5s=1,padding=same	(90,64,m)
输出层	Conv 1D,f_s=5,s=1,padding=same	(90,1,m)

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表 3 廊厅定位误差

定位算法	平均误差(m)	标准差(m)
CNNFi combine	0.25	0.58
CNNFi single	0.73	1.24
CiFi	1.09	1.28
DeepFi	0.99	1.62
PhaseFi	1.10	1.53
ImageFi	0.93	1.34

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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=2	0.48	0.86
CNNFi combine AP=1	0.72	0.86
CNNFi single	1.15	1.22
CiFi	1.36	1.14
DeepFi	1.45	1.21
PhaseFi	1.71	1.43
ImageFi	1.67	1.47

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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
CNNFi	7944414	3264	7947678
Auto Encoder	16121	0	16121
CAE-CNN	490494	1344	491838

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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)
CNNFi	0.1269	0.1399	0.2668	91
CAEFi	0.0062	0.1598	0.1660	5.7898

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