Research On Radar Imaging Method Based On Compressed Sensing Theory

Compressive sensing theory can break through the classical Nyquist sampling theorem to achieve low rate sampling,recover a complete signal by solving a small number of useful information and solve the optimization problem,reduce the amount of computation of signal reconstruction,and ease the pressure of signal acquisition and processing.The theory is also widely used in high-resolution radar imaging,making full use of the sparsity of signals,which can accurately extract the scattering center parameters of targets and effectively improve the quality of radar imaging.Combining compressed sensing theory with radar imaging,this paper uses multiple radars for data fusion to improve the radar imaging resolution,converts multi-radar data fusion problems into sparse solving problems,and conducts in-depth research on sparse algorithms in the case of grid mismatch.And research radar three-dimensional high resolution imaging algorithm.The main contents of this paper are divided into the following aspects:The first aspect introduces the research background and research significance of this paper,expounds the research status of high resolution imaging radar,and illustrates the importance of high-resolution radar imaging based on compressive sensing.The second aspect introduces the data fusion technology.A radar imaging method based on compressed sensing is introduced,and the mesh mismatch(Off-Grid)problem of reconstructing algorithm is analyzed.This problem will lead to incorrect parameter estimation and blurred imaging effect.In the third aspect,the data fusion algorithm of multiple radar is studied.Based on the mesh mismatch model,a joint parameter estimation method is proposed.According to the improved mathematical model,we use the expectation maximization(EM)iterative solution method in sparse Bayesian learning technology to carry out joint parameter estimation,and verify the performance of the algorithm through simulation.In the fourth aspect,the three-dimensional imaging method based on the theory of compressed sensing is studied.Because of the high complexity of the traditional 3D imaging method and the large memory consumption of the computer,the method of dimensionality reduction imaging based on the theory of compressed sensing is studied.and verifies the performance of the algorithm by emulation of the electromagnetic software.The fifth aspect of this paper are summarized,and the next step of the research direction is pointed out.