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基于Wi-Fi感知的多用户身份识别研究

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随着无线感知技术的发展,基于Wi-Fi的身份识别研究在人机交互和家居安防等领域备受关注.尽管基于Wi-Fi信号的身份识别已经取得了初步的成功,但是目前主要适用于用户独立行为场景,并发行为下的多用户身份识别仍然面临着一系列挑战,包括用户之间的相互干扰以及模型鲁棒性差等问题.因此,提出了一种并发行为下多用户身份识别系统Wiblack,其核心思想是训练一个多分支深度神经网络(Wiblack-Net)来提取每个单用户的独特特征.首先,利用主干网络提取多用户之间的共同特征;然后,为每个用户分配一个二分类器以此判断给定群体中是否存在目标用户,在此基础上基于并发行为实现多个用户身份识别.此外,将Wiblack与多个独立的二分类模型和单个多分类模型进行对比实验,对运行效率和系统性能进行分析.实验结果显示,在同时识别3 个用户身份时,Wibalck平均准确率达到了 92.97%,平均精确度为 93.71%,平均召回率为 93.24%,平均F1 值为 92.43%.
Research on multi-user identity recognition based on Wi-Fi sensing
With the advancement of wireless sensing technology,research on Wi-Fi-based identity recognition has gar-nered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success,it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges,in-cluding issues related to mutual interference between users and poor model robustness.Therefore,a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network(Wiblack-Net)to extract unique features for each individual user.Firstly,the common features among multiple users were extracted using the backbone network.Then,a binary classifier was assigned to each user to determine the presence of the target user within a given group,thereby achieving identity recognition for multiple users based on concurrent behavior.In addition,experiments comparing Wiblack with several independent binary classifi-cation models and a single multiclassification model were conducted to analyze operational efficiency.System perfor-mance experimental results demonstrate that when simultaneously identifying the identities of three users,Wibalck achieves an average accuracy of 92.97%,an average precision of 93.71%,an average recall of 93.24%,and an average F1 score of 92.43%.

Wi-Fi sensingchannel state informationidentity recognitionmulti-user recognitionmulti-branch deep neural network

魏忠诚、陈炜、董延虎、连彬、王巍、赵继军

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河北工程大学信息与电气工程学院,河北 邯郸 056038

河北省安防信息感知与处理重点实验室,河北 邯郸 056038

河北工程大学水利水电学院,河北 邯郸 056038

Wi-Fi感知 信道状态信息 身份识别 多人识别 多分支深度神经网络

河北省研究生示范课程建设项目河北省研究生示范课程建设项目河北省教育厅科研项目河北省省级研究生创新项目河北省省级研究生创新项目邯郸市科学技术研究与发展计划

KCJSX2022090KCJSX2022091QN-2020193CXZZBS2022024CXZZSS202409821422031288

2024

10.11959/j.issn.2096-3750.2024.00381

物联网学报
人民邮电出版社有限公司

物联网学报

ISSN：2096-3750
年,卷(期)：2024.8(1)
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