高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于信道状态信息幅值-相位的被动式室内指纹定位

江小平 王妙羽 丁昊 李成华

江小平, 王妙羽, 丁昊, 李成华. 基于信道状态信息幅值-相位的被动式室内指纹定位[J]. 电子与信息学报, 2020, 42(5): 1165-1171. doi: 10.11999/JEIT180871
引用本文: 江小平, 王妙羽, 丁昊, 李成华. 基于信道状态信息幅值-相位的被动式室内指纹定位[J]. 电子与信息学报, 2020, 42(5): 1165-1171. doi: 10.11999/JEIT180871
Xiaoping JIANG, Miaoyu WANG, Hao DING, Chenghua LI. Passive Fingerprint Indoor Positioning Based on CSI Amplitude-phase[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1165-1171. doi: 10.11999/JEIT180871
Citation: Xiaoping JIANG, Miaoyu WANG, Hao DING, Chenghua LI. Passive Fingerprint Indoor Positioning Based on CSI Amplitude-phase[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1165-1171. doi: 10.11999/JEIT180871

基于信道状态信息幅值-相位的被动式室内指纹定位

doi: 10.11999/JEIT180871
基金项目: 国家自然科学基金(61402544),中南民族大学中央高校专项(CZQ14001),湖北省自然科学基金(2017CFB874),中央高校基本科研业务费专项(CZY17001)
详细信息
    作者简介:

    江小平:男,1974年生,副教授,研究方向为信号与信息处理

    王妙羽:女,1994年生,硕士生,研究方向为通信与信息系统

    丁昊:男,1980年生,讲师,研究方向为图像信号处理,神经网络和压缩感知

    李成华:男,1972年生,副教授,研究方向为计算机应用

    通讯作者:

    王妙羽 2016110191@mail.scuec.edu.cn

  • 中图分类号: TN911.7

Passive Fingerprint Indoor Positioning Based on CSI Amplitude-phase

Funds: The Natural Science Foundation of China (61402544), Central South University for Nationalities of China Central University Special Project (CZQ14001), The Nature Science Foundation of Huibei Province (2017CFB874), The Fundamental Research Funds for the Central University (CYZ17001)
  • 摘要:

    基于信道状态信息(CSI)的室内定位技术近几年备受关注。已提出的室内定位方案主要在适用性和定位精度等方面进行不断地创新和改进。该文提出一种被动式的1发2收指纹室内定位系统。用两个固定接收端采集CSI数据,信号预处理阶段对CSI幅值进行奇异值去除与低通滤波,用线性拟合的方法对CSI相位进行校正,将两个接收端采集处理得到的CSI幅值和相位信息共同作为指纹,最终通过全连接神经网络对指纹样本进行训练,并与采集到的实时数据进行匹配识别。实验表明,采用两个接收端以及幅值和相位结合定位的方法,匹配识别率达到了98%,定位精度达到0.69 m。证明该系统能精确有效地实现室内定位。

  • 图  1  系统流程图

    图  2  预处理结果

    图  3  幅值-相位指纹

    图  4  实验场景平面图

    图  5  匹配识别率

    图  6  定位误差

    图  7  训练数据包数量对系统性能的影响

    表  1  不同算法在不同场景定位误差(m)

    空房间 实验室
    平均误差误差方差平均误差误差方差
    本文方案0.690.36 1.251.01
    PhaseFi0.940.561.811.34
    DeepFi1.080.412.011.01
    CSI-MIMO1.550.622.701.42
    下载: 导出CSV
  • ZHUANG Yuan, YANG Jun, LI You, et al. Smartphone-based indoor localization with bluetooth low energy beacons[J]. Sensors, 2016, 16(5): No. 596. doi: 10.3390/s16050596
    BANDIRMALI N and TORLAK M. ERLAK: On the cooperative estimation of the real-time RSSI based location and k constant term[J]. Wireless Personal Communications, 2017, 95(4): 3923–3932. doi: 10.1007/s11277-017-4032-7
    KOO J and CHA Hojung. Localizing WiFi access points using signal strength[J]. IEEE Communications Letters, 2011, 15(2): 187–189. doi: 10.1109/LCOMM.2011.121410.101379
    LI Jinsong, LI Yunzhou, and JI Xinsheng. A novel method of Wi-Fi indoor localization based on channel state information[C]. The 8th International Conference on Wireless Communications & Signal Processing, Yangzhou, China, 2016: 1–5.
    WU Yang, GONG Liangyi, MAN Dapeng, et al. Enhancing the performance of indoor device-free passive localization[J]. International Journal of Distributed Sensor Networks, 2015, 2015: 256162. doi: 10.1155/2015/256162
    WANG Xuyu, GAO Lingjun, MAO Shiwen, et al. DeepFi: Deep learning for indoor fingerprinting using channel state information[C]. 2015 IEEE Wireless Communications and Networking Conference, New Orleans, USA, 2015: 1666–1671.
    WANG Xuyu, GAO Lingjun, and MAO Shiwen. CSI phase fingerprinting for indoor localization with a deep learning approach[J]. IEEE Internet of Things Journal, 2016, 3(6): 1113–1123. doi: 10.1109/JIOT.2016.2558659
    ZHOU Rui, LU Xiang, ZHAO Pengbiao, et al. Device-free presence detection and localization with SVM and CSI fingerprinting[J]. IEEE Sensors Journal, 2017, 17(23): 7990–7999. doi: 10.1109/JSEN.2017.2762428
    CHAPRE Y, IGNJATOVIC A, SENEVIRATNE A, et al. CSI-MIMO: Indoor Wi-Fi fingerprinting system[C]. The 39th Annual IEEE Conference on Local Computer Networks, Edmonton, Canada, 2014: 202–209.
    YANG Zheng, ZHOU Zimu, and LIU Yunhao. From RSSI to CSI: Indoor localization via channel response[J]. ACM Computing Surveys, 2013, 46(2): No. 25. doi: 10.1145/2543581.2543592
    WU Chenshu, YANG Zheng, and LIU Yunhao. Smartphones based crowdsourcing for indoor localization[J]. IEEE Transactions on Mobile Computing, 2015, 14(2): 444–457. doi: 10.1109/TMC.2014.2320254
    WANG Yan, LIU Jian, CHEN Yingying, et al. E-eyes: Device-free location-oriented activity identification using fine-grained WiFi signatures[C]. The 20th Annual International Conference on Mobile Computing and Networking, Maui, USA, 2014: 617–628.
    ZHOU Yiwei, ZHU Hongzi, XUE Hua, et al. Perceiving accurate CSI phases with commodity WiFi devices[C]. IEEE NFOCOM 2017-IEEE Conference on Computer Communications, Atlanta, USA, 2017: 1–9.
    WANG Xuyu, YANG Chao, and MAO Shiwen. TensorBeat: Tensor decomposition for monitoring multiperson breathing beats with commodity WiFi[J]. ACM Transactions on Intelligent Systems and Technology, 2018, 9(1): No. 8. doi: 10.1145/3078855
    BRUNATO M and BATTITI R. Statistical learning theory for location fingerprinting in wireless LANs[J]. Computer Networks, 2005, 47(6): 825–845. doi: 10.1016/j.comnet.2004.09.004
  • 加载中
图(7) / 表(1)
计量
  • 文章访问数:  2715
  • HTML全文浏览量:  883
  • PDF下载量:  122
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-09-06
  • 修回日期:  2019-09-25
  • 网络出版日期:  2020-01-11
  • 刊出日期:  2020-06-04

目录

    /

    返回文章
    返回