摘要
由于室内环境复杂多变和多径效应对WiFi传播信号的影响,因此产生了大量的非视距路径,导致信号严重衰落,通信链路质量恶化,造成应用识别精度不高、实现系统复杂等问题。文中提出了一种基于CSI的非视距信号识别方法NWI(NLOS recognition based on Wavelet Packet Trans form Identification),主要用于对WiFi的物理层信息——CSI信号进行特征提取,识别当前链路中是否存在遮挡。所提方法对CSI信号的幅值进行三层小波包分解,分别提取第3层8个频段的小波包系数、小波包能量谱、信息熵和对数能量熵作为特征向量,利用支持向量机进行分类,从而识别出非视距信号。相比其他方法,该方法无须对CSI信号进行预处理,最大程度地保留了环境对传播信号的影响,更真实地反映室内环境。实验结果表明,该方法在动态环境中的识别精度为96.23%,在静态环境中的识别精度为94.75%,证明了基于小波包变换的CSI信号特征提取方法能够有效识别非视距信号,具有较高的识别精度和普适性。
Due to the influence of complex and changeable indoor environment and multipath effects on WiFi signal propagation,a large number of non-line-of-sight paths are generated,which lead to serious signal fading and communication link quality dete-rioration,resulting in low recognition accuracy and complex system implementation.In this paper,a CSI-based NWI(NLOS recognition based on Wavelet Packet Transform Identification)is proposed,which is mainly used for feature extraction of CSI signals,the physical layer information of WiFi,to identify whether there is blocking in the current link.The three-layer wavelet packet is used to decompose the amplitude of CSI signal,the wavelet packet coefficients,wavelet packet energy spectrum,information entropy and logarithmic energy entropy of 8 frequency bands in the third layer are extracted as feature vectors,and the support vector machine is used for classification.Thereby a non-line-of-sight signal is identified.Compared with other methods,the proposed method does not need to pre-process the CSI signal,and the influence of the environment on transmission signals is maximum retained,so as to reflect the indoor environment more realistically.The experimental results show that the recognition accuracy of the proposed method is 96.23%in the dynamic environment and 94.75%in the static environment.It is proved that the CSI signal feature extraction method based on wavelet packet transform can effectively identify non-line-of-sight signals and has high recognition accuracy and universality.
作者
田春元
余江
常俊
王彦舜
TIAN Chun-yuan;YU Jiang;CHANG Jun;WANG Yan-shun(Department of Information,Yunnan University,Kunming 650500,China)
出处
《计算机科学》
CSCD
北大核心
2020年第11期327-332,共6页
Computer Science
基金
国家自然科学基金(61162406)
云南省高校频谱传感与边疆无线电安全重点实验室开放课题(C6165903)
云南大学研究生科研创新项目(Y2000211)。
关键词
信道状态信息
非视距信号识别
小波包变换
特征提取
支持向量机
Channel state information
Non-line-of-sight signal recognition
Wavelet packet transform
Feature extraction
Support vector machine
