Research On Main-lobe Interference Suppression Algorithm Of Phased Array Radar

Phased array radar,is widely used because of its long detection range,adaptive anti-jamming,fast target recognition and tracking performance.Array signal processing is the theoretical basis of phased array radar,and adaptive beamforming is an important theoretical branch.At present,the theoretical results of adaptive beamforming for sidelobe interference suppression are quite rich.However,when the interference enters from the main-lobe of the pattern,the traditional adaptive beamformer often has the problems of main-lobe distortion,peak offset and sidelobe level rise.In order to solve the above problems,the main-lobe interference suppression algorithm of phased array radar is studied in this paper.Firstly,according to the transmission characteristics of far-field narrowband signal,the mathematical model of array received signal is established,the basic principle of adaptive beamforming is introduced,and three commonly used adaptive weight optimization criteria are elaborated.In this part,the definition of main-lobe interference is given.The influence of main-lobe interference on adaptive beamforming is studied through theoretical analysis and simulation,and the causes of the influence are studied.This part lays a theoretical foundation for the subsequent research on the main-lobe interference suppression algorithm.Secondly,the main-lobe interference suppression algorithm is studied based on the Blocking Matrix Processing algorithm and the Eigen-Projection Matrix Processing algorithm.The main-lobe interference suppression mechanism of two kinds of preprocessing algorithms is introduced,and the interference suppression performance is analyzed.Aiming at the problem that blocking matrix leads to the performance degradation of beamforming,two improved algorithms,Diagonal Loading method and Linear Constraint method are introduced.The performance improvement of the algorithms are verified by a number of simulations.Aiming at the problem that the performance of beamforming is degraded due to the characteristic projection matrix,an improved algorithm based on Covariance Matrix Reconstruction is introduced.Aiming at the problem that the interference suppression performance of eigen-projection matrix decreases when the desired signal exists in the training sequence,an improved algorithm for constructing the interference and noise covariance matrix by Iteration Adaptive method is introduced.The interference suppression performance and beamforming performance of the two improved algorithmsare analyzed bysimulations.Finally,aiming at the problem of interference suppression performance degradation of Eigen-Projection Matrix algorithm under the condition of low sampling points and non-Gaussian distribution of sampling sequence,the Eigen-Projection Matrix method based on Convex structure Taylor estimation is proposed.Based on the structural characteristics of the signal covariance matrix,the convex structural constraints are combined with Taylor's estimator.The weight iteration of the steering vector matrix is used to replace the iteration of the whole covariance matrix.The estimation accuracy of the covariance matrix is improved,the main-lobe interference suppression performance of the eigen-projection matrix is enhanced,and the robustness of the algorithm is improved.Aiming at the problem of interference suppression performance and beamforming ability degradation when there has expected signal in the sampling training sequence,the interference and noise covariance matrix is constructed by combining spatial spectrum estimation,and the beamforming algorithm using linear constraints to obtain adaptive weights is improved.The simulation results show that the proposed algorithm has robust main-lobe and sidelobe interference suppression performance and robust adaptive beamforming ability.