High-resolution Imaging Of MIMO Radar Sparse Array

Multiple Input Multiple Output(MIMO)radar is a new system radar that has recently received extensive attention.Compared with traditional phased array radar,MIMO radar has significant advantages in target detection,angle measurement,and low main lobe intercept probability.However,while it has high performance,it also brings a lot of pressure to the radar system regarding signal processing and data storage.In recent years,the Compressed Sensing(CS)theory is a rapidly developing signal processing theory that can obtain the original signal through fewer measurement values.Applying compressed sensing theory to MIMO radar signal processing can make the data easier to transmit and store and simplify the radar system structure design in the antenna array.This thesis studies the sparse highresolution imaging method based on the MIMO system and discusses the optimization of the transmit waveform and the arrangement of the cognitive array.The main work contents are as follows:1.A direction of arrival(DOA)estimation method based on sparse restoration is studied.Because of the poor high resolution of traditional radar azimuth imaging side lobes,the performance of different sparse recovery algorithms in DOA estimation is discussed.Compared with the traditional DOA estimation method,the method based on sparse restoration has higher accuracy and better resolution.Finally,the performance indicators such as super-resolution and anti-noise ability of different sparse restoration algorithms in DOA estimation are analyzed by simulation.2.A MIMO radar waveform optimization method based on a sparse restoration framework is studied.In order to solve the problem of low resolution of range-dimensional imaging,an adaptive waveform optimization method is discussed.Different distance units are gridded to establish a distance dictionary.The waveform optimization problem is transformed into a dictionary correlation optimization problem.The optimized dictionary can reconstruct the distance dimension of the target scattering point.Considering the changes brought by the scene changes to the algorithm,the contour information and structure information of the imaging target are added to the algorithm for feedback.The dictionary’s range and weight are adaptively optimized to realize the adaptive waveform based on the dynamic target scene.Finally,simulation verified that the waveform optimized by the proposed algorithm has improved imaging quality.With the dynamic changes of the imaging scene,it can also be adaptively optimized to match the current scene.3.A cognitive antenna selection method for MIMO radar is studied.Aiming at the problem of many channels and enormous computational complexity in MIMO radar imaging,this method optimizes the array arrangement under a given number of array elements.It is assumed that the initial imaging results are obtained with a conventional uniform linear array.Then at the next moment of imaging,the accuracy of the low-resolution image is gradually improved according to the cognitive paradigm through specific antenna position selection.Thanks to the improved accuracy,the support domain of the target can be estimated,the dimension of the undersampling matrix can be reduced,and finally,the amount of computation in the subsequent high-resolution image reconstruction process can be reduced.The simulation results show the imaging performance comparison under different array elements and signal-to-noise ratio,which shows that the cognitive antenna selection method has better imaging ability than random antenna selection.