Human Detection Based On Channel State Information And Visibility Graph

With the development of science and technology,wireless local area networks have been widely deployed in various scenarios due to their advantages of low cost,convenience,flexibility,and ease of planning and adjustment.Researchers on wireless signal applications are gradually increasing,including wireless signals.Research on the state of the human body has received much attention as an important branch of many applications.In this paper,the channel state information CSI of the physical layer of the wireless local area network is used as the original data,and combined with the theoretical technology of the viewable algorithm in the complex network,the passive population statistics classification and the human body orientation detection method based on the channel state information and visibility graph are designed.The research work of this paper mainly includes the following aspects:(1)Analyzed the formation principle of CSI and its relative RSSI advantages,and introduced the research status of human condition detection based on CSI;(2)Explain the implementation principle of multiple viewable algorithms in complex network theory and its background,and introduce the properties and application of different visual algorithms;(3)The preprocessing and feature extraction methods of CSI data are studied to make the data more effective and reliable,and reduce the influence of other factors;(4)Comparing CSI samples into time series,a CSI-based viewable network method is proposed to extract network features and integrate original statistical features to further improve the detection effect;(5)The machine learning and deep learning algorithms are introduced,and combined with channel state information,a variety of different networks are constructed to extract representative network features;(6)The experiment considers the influence of different factors such as environment on the detection effect,and verifies the feasibility of constructing the hybrid network based on the channel state information to realize the human body state detection.