Research On The Application Of Compressive Sensing In Target Location For MIMO Radar

As a new radar system,MIMO radar is the research hotspot of radar processing technology.But in the process of introducing the traditional radar target localization methods to the MIMO radar target location methods,we found that the MIMO radar signal becomes larger in scale because of the traditional positioning method.This vast amount of data is subject to the traditional Nyquist(Nyquist)sampling theorem,which poses a great challenge to the real-time processing of signals.The theory of compressed sensing effectively utilizes the sparsity of signals,and breaks the bottleneck of Nyquist sampling theorem,so that the amount of sampled data is far less than the amount of data required by traditional sampling methods.The distributed compressed sensing(DCS)change sparse of a single signal in one base to sparse of multiple signals in a number of bases.It uses signal correlation to achieve joint reconstruction of multiple signals under a small amount of sampling.This feature can be applied to target localization of MIMO radar with the same characteristics.In this paper,using the advantages of distributed compressed sensing,Joint Subspace Pursuit is proposed for lack of the high sampling rate in traditional radar target localization method.The main works are as follows:Firstly,the realization process and shortcomings of traditional radar positioning method are analyzed.Then,the framework of compressed sensing is introduced in detail.The performance of several common reconstruction algorithms is analyzed and compared.After that,The optimal point of signal reconstruction using compressed sensing is pointed out and a new structure of perception matrix is proposed.Simulation results show that the proposed matrix can effectively reduce the time required for signal restoration.Secondly,the principle and signal characteristics of MIMO radar are studied,and the signal model of MIMO radar is established.The localization problem of MIMO radar is transformed into sparse matrix reconstruction in compressed sensing theory.The composition of the joint sparse model is studied,and the correlation information between the signals of MIMO radar is fully utilized,so that less measurements contain sufficient information,the computational complexity is further simplified.Finally,given the common sparse support of the second joint sparsity model,Joint Subspace Pursuit is proposed.In each iteration,it selects the index of the base vector that maximizes the value of the sum of the magnitudes of the projections of the residual,and updates the estimate of the coefficients for all the selected base vectors.Simulation results show that joint subspace pursuit algorithm based on distributed compressed sensing has better localization performance in MIMO radar target location.