Research On Indoor Through-the-wall Passive Moving Target Detection Algorithm Based On Channel State Information

Through-the-wall target detection technology has important application value in many fields such as smart home,security,and disaster rescue.Especially with the rapid development of wireless communication and Internet of Things,designing a low-power,small-volume through-the-wall target detection system suitable for indoor scenes has become an urgent technical requirement.Since Wi Fi devices are widely deployed in various indoor places,the through-the-wall target detection technology based on Wi Fi channel state information has received widespread attention.The existing through-the-wall moving target detection algorithms based on Wi Fi channel state information have the following two problems: 1.The extracted signal features are relatively single,which can only detect the presence or absence of moving targets,but not distinguish the number of targets in more detail.2.Due to the unsatisfactory clutter mitigation effect,the detection accuracy of the same-side through-the-wall target detection algorithm needs to be further improved.In view of the above problems,through-the-wall moving target detection algorithms in the two cases of opposite side of transceivers and the same side of transceivers are studied in this thesis.The main research contents are as follows: 1.A multidimensional signal feature extraction method is proposed.Firstly,a linear error cancellation method is used to correct the phase based on the analysis of the channel initial phase difference,clock synchronization error,carrier frequency error and sampling error.Secondly,the outliers are replaced to overcome the sudden interference,and the environmental noise is removed by the wavelet threshold method.Finally,the correlation of the signals is analyzed in the time domain and the subcarrier domain,respectively,and the robust features that are independent of the transmission power are fully extracted from the amplitude correlation coefficient matrix and the phase correlation coefficient matrix,and the features are fused by neural network.2.A clutter mitigation method based on the array flow pattern reconstruction of strong signal is proposed.First,a single-dimensional multiple signal classification algorithm model is established in the frequency domain using channel state information of a single subcarrier.Second,in order to overcome the insufficient physical antennas,the model is extended to multiple subcarriers,and the subcarriers are used as virtual antennas to expand the number of receiving elements.Third,the two-dimensional smoothing method is used to perform decoherence to realize the two-dimensional joint parameter estimation of angle of arrival and time of flight.At last,the parameter corresponding to the strong peaks is extracted from the spectral function to reconstruct the array flow pattern of the interference signal,and it is removed from the received signal to complete the clutter mitigation.The detection platform is built to verify and analyze the algorithm in indoor environments with glass walls,brick walls,and concrete walls in this thesis.The experimental results show that the detection accuracy of through-the-wall moving target detection algorithm for the opposite side of transceivers in each environment is above 0.98,0.90,and 0.85,and the detection accuracy rate of through-the-wall moving target detection algorithm for the same side of transceivers is above 0.93,0.89,0.84.Compared with the existing algorithms,the proposed algorithm has higher detection accuracy and finer detection granularity.