线性约束和协方差矩阵重构的主瓣干扰抑制算法研究

he algorithm of mainlobe interference suppression based on eigen-projection and covariance matrix reconstruction (EP-CMR) can effectively suppress the mainlobe interference and sidelobe interference existing in the array antenna receiving data. However, when the power of mainlobe interference is higher, the sidelobe interference's null steering has become shallow or even disappeared, which will decrease the performance of the sidelobe interference suppression. In accordance with the EP-CMR, an improved algorithm based on linear constraints and covariance matrix reconstruction (LC-CMR) is proposed in this paper. Firstly, the adaptive weight vector and reconstructed echo data can be obtained by the reconstructed covariance matrix; Secondly, the liner constraints is used to certify beam pattern nulling in the sidelobe interference direction, and the least square fitting algorithm is used to modify the adaptive weight vector, which makes the modified weight vector satisfy the linear constraints; Thirdly, the output of the adaptive beamforming can be calculated by the modified weight vector and processed echo data. Simulation results have shown that the proposed algorithm in this paper can ensure the deeper null steering in the sidelobe interference direction while effectively suppressing the mainlobe interference, and thus raise the output SINR of the system in the case of the higher power of the mainlobe interference.