摘要
现有的WiFi感知方法对数据的采集量及接收器的硬件资源需求较高,同时海量的数据处理也会消耗大量软硬件资源.基于模型的WiFi感知方法通过建立动作模式与信号变化之间的数学模型,一定程度上降低了对数据量的依赖,但是主流方案仍然需要多个接收天线或者天线阵列.本文提出一种使用单天线接收器的感知方案,利用不同子载波的信道状态信息的比值消除硬件及噪声干扰,并提出基于方差和极差的子载波组合选择算法,筛选出优质的子载波组合得到动作特征.进一步提出基于菲涅尔区理论的高可用特征生成算法,巧妙地结合反射路径变化与信道状态信息(Channel State Information,CSI)动态相位旋转之间的关系,通过在复平面上的数据拟合与相位对齐获得高可用特征.理论分析和实验结果表明,本文提出的单天线方案完全符合菲涅尔区理论,同时,可以有效提升不同动作在不同场景下的识别效果.对于七种不同的动作,本文方案的总体识别准确率保持在95%左右,CSI选择和特征强化获得了约2%的准确率提升.
The existing WiFi sensing methods have high requirements for data collection and receiver hardware resources.At the same time,massive data processing will also consume a lot of hardware and software resources.The modelbased WiFi sensing method reduces the dependence on the amount of data to a certain extent by establishing a mathematical model between the action mode and the signal change,but the mainstream solution still requires multiple receiving antennas or antenna arrays.This paper proposes a sensing scheme using a single-antenna receiver for the first time,using the ratio of channel state information(CSI)of different subcarriers to eliminate hardware and noise interference,and proposes a subcarrier combination selection algorithm based on variance and range to screen out high-quality subcarriers combination to get action features.A high-availability feature generation algorithm based on Fresnel zone theory is further proposed,which skillfully combines the relationship between reflection path changes and CSI dynamic phase rotation,and obtains high-availability features through data fitting and phase alignment on the complex plane.Both theoretical analysis and experimental results show that the single-antenna scheme proposed in this paper fully complies with the Fresnel zone theory,and at the same time,it can effectively improve the recognition effect of different actions in different scenarios.For the seven different actions in this paper,the overall recognition accuracy of the scheme is maintained at about 95%,and CSI selection and feature enhancement achieved an accuracy improvement of approximately 2%.
作者
姜禹
王佳东
胡爱群
丁璠
JIANG Yu;WANG Jia-dong;HU Ai-qun;DING Fan(School of Cyber Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China;Purple Mountain Laboratories,Nanjing,Jiangsu 210096,China;Key Laboratory of Computer Network Technology of Jiangsu Province,Nanjing,Jiangsu 210096,China;State Key Laboratory of Mobile Communication,Southeast University,Nanjing,Jiangsu 210096,China;Frontiers Science Center for Mobile Information Communication and Security,Southeast University,Nanjing,Jiangsu 210096,China;School of Transportation,Southeast University,Nanjing,Jiangsu 210096,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2024年第10期3409-3423,共15页
Acta Electronica Sinica
基金
国家重点研发计划项目(No.2022YFB4300300)。
