Azimuth And Range Measurement Based On Sparse Reconstruction For Integrated Communication And Radar System

The radar-communication integration system,which can realize the fusion of radar detection and wireless communication,is widely used in vehicle communication,wireless sensor network and electronic countermeasure.And the radcom system in vehicle communication environment combined with orthogonal frequency divided multiplexing(OFDM)technique,aims to realize the communication between the cars,and the perception of vehicles' position.In the detection plane,we can get the range and the azimuth of the target vehicle to roughly determine the location of it.Therefore,this.paper conducts a study of range and azimuth measurement with radar-communication integration signal in vehicle communication environment.Traditional parameter estimation methods,such as subspace decomposition algorithms,have very strict requirements on the signal-to-noise ratio(SNR)and the number of snapshots.The source number is also needed to be known as a priori knowledge.In recent years,with the gradual improvement of sparse reconstruction theory,a lot of domestic and foreign scholars pay much attention to it.They have applied it to the source localization,and has made a good achievement.Based on the radar communication integration system,this paper discusses the estimation of angle and distance from the perspective of sparse reconstruction.(1)The angle estimation problem based on sparse reconstruction is studied.l1 norm constraint is often used for sparse signal sparsity constraint.However,it is not the most appropriate sparsity constraint.Under low SNR,using the l1 norm method to reconstruct spatial spectrum will appear pseudo peaks,resulting in performance degradation.To solve this problem,a new weighted l1 norm angle estimation methods is proposed in this paper.We construct sparse model by the largest eigenvector,aiming at reducing computational complexity.Furthermore,when the source number estimation is error,the proposed method is still valid.In addition,weighted matrix based on eigenvector is designed,which promotes the sparsity of spatial spectrum,and improves the accuracy of angle estimation.(2)We make a research on joint angle and range estimation based on sparse reconstruction.For multiple parameters,the number of columns in two-dimensional dictionary matrix is too large,leading to a significant increase in the reconstruction complexity.To solve this problem,we proposed a new sparse model to reduce the amount of computation.Note that OFDM signal can be realized by discrete Fourier transform.So we can get the frequency domain signal of array received signals.Then we deal with it to get a parameter matrix comprising angle and range.The parameter matrix is preprocessed to separate the angle and range.And the two-dimensional dictionary matrix is converted into one-dimensional dictionary matrix to reduce the computational complexity.Simulation results show that the proposed algorithms can meet the accuracy requirements of vehicle location,and has the advantages of high resolution,strong noise robustness and suitable for coherent signals.