Research On Mainlobe Interference Suppression Algorithm Of Array Signal

Adaptive beamforming,which is able to enhance the desired signal while suppressing the interferences without any prior information of the interferences,has wide applications in radar,sonar,medical imaging,radio astronomy,and other fields.Sidelobe interferences can be suppressed by adaptive beamforming,effectively.However,when the interference falls into mainlobe,adaptive beamforming will cause distortion and peak offset of mainlobe and heightening of sidelobe level,et al.For these problems,the main contents of this paper are summarized as follows:Firstly,the principle of array signal adaptive beamforming interference suppression is introduced in detail under the mathematical model of far field.The common adaptive weight vector optimization criteria are described and the different points of adaptive beamforming techniques are analyzed between with sidelobe interferences suppression and mainlobe interferences suppression as well.In order to analyze the specific influence of mainlobe interferences on radar system,a mathematical model of different mainlobe interferences are established at the same time.When the interference is located in mainlobe,the traditional adaptive beamforming will form a null in mainlobe to suppress the interference,which leads to the deterioration of the adaptive beamforming performance.In order to overcome these shortcomings,the Blocking Matrix Preprocessing(BMP)based algorithm and Eigen-projection Matrix Preprocessing(EMP)based algorithm is discussed,respectively.On the one hand,the influence of on array signal preprocessing is discussed after preprocessing by Blocking Matrix Preprocessing mainlobe interference suppression algorithm.After that,an improved algorithm based on Diagonal Loading(DL)and Linear Constraint(LC)is introduced and the performance of the improved algorithm is verified in different simulation environments.On the other hand,Eigen-projection Matrix Preprocessing algorithm is deeply studied in this paper,which is divided into two improvement directions: constraint and covariance matrix reconstruction.Then,the Eigen-projection Matrix Preprocessing and Covariance Matrix Reconstruction(EMP-CMR)and the Eigen-projection Matrix Preprocessing and Covariance Matrix Integral Reconstruction(EMP-CMIR)is introduced atthe same time.Based on EMP-CMR and EMP-CMSR,the performance of interference suppression is verified in different simulation environments.Finally,the in-depth study is proceeded aiming at the lack of robustness and the poor performance of interference suppression in the beamforming based on Eigen-projection Matrix Preprocessing.Then,the Eigen-projection Matrix Preprocessing and L2 norm constraint(EMP-L2)and Eigen-projection Matrix Preprocessing and Covariance Matrix Sparse Reconstruction(EMP-CMSR)is proposed.Compared with EMP-CMR method,the output Signal to Interference plus Noise Ratio(SINR)of EMP-L2 algorithm is lower,however,it has a roubst performance with array mismatch and error.In the case of limited computing resources has obvious advantages.EMP-CMIR use Capon power spectrum to sparse reconstruct the interference-plus-noise covariance matrix.However,EMP-CMIR has disadvantages of Capon power spectrum energy leakage and high computational complexity of integral reconstruction.In order to overcome these shortcomings,using the idea of Compressed Sensing(CS)to reconstruct the interference-plus-noise covariance matrix,the Eigen-projection Matrix Preprocessing and Covariance Matrix Sparse Reconstruction(EMP-CMSR)is proposed in this paper.Compared with EMP-CMIR method,the proposed method can achieve more effectiveness,more robustness,higher output SINR and lower computational complexity.