Research On Environment Sensing And Imaging System Based On Channel State Information

Integrated sensing and communication(ISAC)is one of the key technologies of the nextgeneration mobile communication system,which will allow the two independent functions of wireless communication and wireless sensing to be integrated and fused with each other on the same hardware system,the same spectrum resource,and signal processing algorithms.Therefore,while performing high-quality communication,the next-generation wireless network can also perform high-precision sensing tasks,and the communication and sensing functions cooperate with each other,and ultimately improve the overall performance of the network.An important implementation of ISAC is passive sensing based on channel state information(CSI).Without making major adjustments to the hardware and software of communication systems,CSI data obtained from periodic channel estimation is used to realize sensing and imaging of environment targets,which has a wide application potential.This thesis mainly studies the CSI-based 3D environment sensing and imaging system,including channel model,imaging algorithms,interference cancellation,through-the-wall imaging and other challenging issues.The main research contents are summarized as follows:First,this thesis proposes a CSI-based scheme for 3D environment sensing and imaging.The existing sensing work based on CSI data of Wi Fi system is generally limited to the detection and estimation of a small number of parameters,rarely involving imaging tasks? and microwave computational imaging system requires special and expensive hardware equipment,which is difficult to be compatible with communication systems.Therefore,this thesis attempts to combine the advantages of these two types of systems,combine the convenience of CSI data acquisition with the accuracy of computational imaging systems,and achieve low-cost and high accuracy imaging systems by using a large number of CSI data with high spatial and frequency freedom.Specifically,the sensing model between the environment target and the channel response is established by using geometric optics,ray tracing and other methods.With the inherent sparsity of environment target,the imaging problem is modeled as a generalized compressive sensing optimization problem,then an imaging algorithm based on alternate optimization and approximate message passing mechanism is designed.Simulation results show that the proposed algorithm has excellent imaging performance and fast convergence speed.This scheme has certain reference value for the sensing research of the next generation wireless network.Second,this thesis studies the problem of interference cancellation in imaging systems.The imaging system is faced with a variety of interferences,and the interference caused by the multiple scattering of the detection signal between the target objects is the most important one,which will reduce the signal to interference plus noise ratio of the system and seriously affect the imaging performance.To solve this problem,this thesis proposes an strategy of interference estimation and iterative interference cancellation,that is,reconstructing the environment targets first,then estimating the interference values,and iterating in two steps until convergence.In addition,an adaptive rounding and weighting scheme is designed to improve the system convergence.The experimental results show that the proposed algorithm can effectively eliminate the interference in the channel data,thus achieving better imaging performance,and the computational efficiency is also high.This work can provide a potential solution to the interference problem faced by imaging systems.Third,this thesis proposes a through-the-wall imaging scheme based on indoor and outdoor equipment cooperation.The system has both indoor user equipment and outdoor base station facilities.It uses CSI data obtained during the communication process between users and base stations to achieve through-the-wall imaging of indoor targets.One of the main challenges of the through-the-wall imaging problem is wall compensation when parameters are unknown.Specifically,the existence of the wall will cause changes in the propagation path and velocity of electromagnetic waves,and lead to a significant decrease in signal strength.If the wall impact is not compensated,the imaging results will appear artifacts,position shift or loss of details.When the parameters such as the position and electromagnetic characteristics of the wall are unknown,the problem of wall compensation becomes more difficult.To solve this problem,this thesis decomposes the problem of through-the-wall imaging into two steps: wall parameter estimation and target imaging.In the stage of wall parameter estimation,the position and transmission coefficient of the wall are estimated by means of global search and least squares parameter estimation.In the target imaging stage,the wall parameters are compensated first,and then the improved EM-GAMP algorithm is used to solve the imaging problem.The Simulation experiments prove that the proposed algorithm can accurately estimate wall parameters and efficiently reconstruct indoor objects.