Research On Moving Target Detection And Imaging Of Multi-channel Synthetic Aperture Radar

With the continuous development of Synthetic Aperture Radar(SAR)ground moving target identification(GMTI)technology,people have higher and higher requirements for moving target detection and imaging accuracy.Compared with single-channel SAR,multi-channel SAR can acquire more ground echo information,which can more accurately and conveniently suppress clutter and detect moving target information.Therefore,multi-channel SAR is an important platform for GMTI implementation.In order to further improve the accuracy of moving target detection and imaging,the application of machine learning in the field of SAR GMTI has become a research hotspot.However,existing related algorithms have the disadvantages of large computational complexity,inaccurate estimation of fast moving targets,and incomplete estimation of moving target parameters.In order to solve these problems,this paper studies the GMTI method of multi-channel SAR,and improves the SAR GMTI method based on Sparse Bayesian Learning(SBL).The main work of this article includes:1.A multi-channel SAR target echo signal model was established,and the DPCA(Displaced Phase Center Antenna),Along-track SAR interferometry(ATI)and Clutter Suppression Interference were studied.Multi-channel SAR clutter suppression methods such as Clutter Suppression Interferometry(CSI).2.For slow-moving targets,a slow moving target detection method based on CSI and Lv's Distribution(LVD)is studied,and SBL is used to further refine the moving parameters of moving targets.The method first uses the polar coordinate algorithm(Polar Format Algorithm,PFA)to migrate the moving target for correction;then uses the CSI to detect the moving target's range velocity;then uses the LVD to migrate the linear frequency modulation signal(Linear Frequency Modulation).(LFM)performs parameter estimation and further estimates the motion parameters of the moving target;finally,it uses the SBL to refine the estimated parameters so as to obtain a high-accuracy parameter estimation value.3.In order to reduce the amount of computation and facilitate the processing of measured data,a moving target detection method based on ATI and SBL is proposed.The method first uses ATI to estimate the range-to-velocity of the moving target;then the SBL is used to estimate other parameters.The experimental results of simulation and measured data verify the effectiveness of the method.4.For fast-moving targets,the moving target detection methods based on Radon transform(RT)and Fractional Fourier transform(FrFT)are studied.This method first uses Radon transform to detect the range velocity of the moving target,and then uses Fr FT to estimate the azimuth velocity.5.For fast moving targets,in order to reduce the amount of calculation and complete estimation of moving target parameters,the REVISIT GMTI algorithm is proposed.The method firstly uses the fast frequency-azimuth time domain transform(FFST)to eliminate the distance migration,and then uses the LVD to detect the Doppler frequency and the linear modulation frequency in the LFM so as to obtain the moving target.The relevant parameters;finally use the SBL to refine the parameters,so as to obtain high-precision parameter estimates.This method can fully estimate the two-dimensional velocity and two-dimensional spatial position of rapid moving targets,and the computational complexity of this method is significantly reduced.The simulation experiment results verify the effectiveness of the method.