Abstract: At present,Wi-Fi has been widely used in public and private fields.Deviceless human intrusion detection based on wireless technology has a broad application prospect for realizing indoor services such as asset security,emergency response and personalized services.Aiming at the problems of serious false positives and false negatives,difficult analysis of massive information,troublesome deployment,etc. of existing methods,this paper proposes an intrusion detection method based on Wi-Fi signals.Firstly,it uses the fine-grained Channel State Information(CSI) on the Wi-Fi device to capture small changes caused by human movements.Then it uses the Multiple Signal Classification(MUSIC) to sample the covariance matrix eigen decomposition.The obtained noise subspace is used to estimate the target angle(AOA).Finally,the intrusion is judged by calculating the phase difference changes of different paths caused by the movements of the human body.The difference between traditional methods and the proposed method is that the spectral peak search and phase difference are combined,with complementary advantages,the two overcome the environmental and noise interference,and solve the influence of multipath effects on the results of this paper.There are two typical indoor environments in this paper,namely the conference room and the dark room.Experimental results show that the average false negative(FN) and false positive(FP) of the method in the two indoor environments are 1.83% and 1.4%,respectively.In addition,this paper also evaluates the detection performance of the proposed method in different sports modes,and the average false negative and false positive are 2.26% and 1.46%,respectively.By comparing with other methods,the validity and stability of the proposed method are verified.It shows that this method has strong robustness and practical value,and provides a feasible scheme for the development of intrusion detection technology in the future.

Key words: Channel state information, Intrusion detection, Multiple signal classification algorithm, Phase difference, Training-free