基于SAMV-SML的主瓣干扰抑制算法

Mainlobe Interference Suppression Algorithm Based on SAMV-SML

针对主瓣干扰下自适应波束形成出现的波束畸变和主瓣偏移等问题,提出基于随机最大似然稀疏渐进最小方差(Sparse Asymptotic Minimum Variance Stochastic Maximum Likelihood,SAMV-SML)的主瓣干扰抑制算法。利用SAMV-SML算法准确地估计目标方位和功率,重构旁瓣干扰噪声协方差矩阵,避免将期望信号引入权矢量,并根据估计的主瓣干扰方位和期望方位,利用相关系数判断主瓣干扰特征矢量,构造特征投影预处理矩阵,实现多主瓣干扰抑制。仿真实验表明该算法能够实现波束保形,达到更快的收敛速度和更高的输出信干噪比。此外,还验证了该算法具备处理相干主瓣干扰的能力。

To solve the problems of beam distortion and main lobe offset in adaptive beamforming under main lobe interference,main lobe interference suppression algorithm based on sparse asymptotic minimum variance stochastic maximum likelihood(SAMV-SML)is proposed.The SAMV-SML algorithm is used to accurately estimate the target azimuth and power,reconstruct the sidelobe interference noise covariance matrix,so as to avoid introducing the expected signal into the weight vector.According to the estimated main lobe interference azimuth and expected azimuth,the correlation coefficient is used to judge the main lobe interference feature vector,construct the eigen-projection matrix processing matric,and achieve multi main lobe interference suppression.Simulation results show that the algorithm can achieve beam shape preservation,faster convergence speed and higher output signal to interference noise ratio.In addition,the ability of the algorithm to deal with coherent main lobe interference is also verified.