Main-lobe Mixed Jamming Suppression Algorithm For Phased Array Radar

Radar anti-main lobe jamming is a difficult and hot issue in recent years.For phased array radar,the anti-jamming advantages of traditional side lobe cancellation and adaptive beam formation(ADBF)are gone when the jamming enters from the main lobe.As the jamming environment becomes more and more complex,the multi-jamming has replaced the single jamming.Most existing studies on anti-main lobe jamming are aimed at single main lobe jamming and lack of analysis of measured data.Therefore,in this thesis,the main lobe jamming suppression algorithm of phased array radar is studied and modified by theoretical derivation,simulation research and analysis of measured data based on main-lobe mixed jamming background.In airspace domain,aiming at the problems of ADBF main lobe nulling and side lobe distortion caused by main lobe jamming,a new modified covariance matrix reconstruction(MCMR)beam conforming algorithm suitable for BMP is proposed in this thesis by studying the blocking matrix preprocessing(BMP)and eigen-projection matrix preprocessing(EMP)algorithms.The improved algorithm BMP-MCMR corrects the over-processing phenomenon caused by BMP preprocessing,thereby reconstructing the covariance matrix and completing the shape-preserving of the adaptive beamforming pattern.Through the comparison of simulation and measured data results,it is found that BMP-MCMR has better beam conformation ability and better anti-jamming performance than traditional beamconforming algorithms.The main lobe offset correction ability of BMP-MCMR is strong,and the width of the main lobe after conformation is basically the same as the width of the main lobe of the static pattern.The BMP-MCMR has good beam shape-keeping ability and good output SINR when the main lobe jamming’s INR changes,the main lobe jamming angle changes,and the sampling snapshot contains targets.The experimental results are sensitive to change in the number of sampling snapshots,so BMP-MCMR is more stable than others.And MCMR greatly reduces the amount of calculation of matrix inversion,which is beneficial to engineering realization.In space-time domain,based on the environment of main lobe mixed jamming with side lobe jamming,this thesis proposes a new anti-main lobe jamming algorithm based on block SP and change point detection through studying the multi-beamforming with blind source separation algorithm and traditional SP algorithm.It solves the problem that traditional algorithms only use large eigenvalues cannot accurately distinguish the jamming subspace in the case of increasing variance of jamming eigenvalues caused by large differences in jamming intensity and similar main lobe jamming angles.The improved SP algorithm uses the vertical relationship between the noise space and the signal space to calculate the variation coefficient of the block feature interval projection result.Then using MSE change point detection algorithm to determine the source space.Artificial threshold is not required,and it is more robust.On the premise of a single target,different algorithms are used to classify the target and jamming in order to perform accurate vertical projection.Compared with BMP-MCMR,the improved SP algorithm is more computationally intensive,but the experimental results of simulation and measured data verify that the improved SP algorithm has higher accuracy and better anti-jamming performance.Under the same parameters,the output SINR is not much different from the BMP-MCMR unbiased estimation,and it is higher than BMP-MCMR biased estimation.Improved SP algorithm does not need to estimate the jamming angle,which is more robust.Finally,through theoretical derivation,simulation experiments,and analysis of measured data,this thesis gives an analysis of the applicability,advantages and disadvantages of the existing algorithms and the improved algorithms.It provides a theoretical basis for the selection of anti-main lobe jamming algorithms in different jamming environments.