Strong Mainlobe Interference Suppression Method Based on Eigen-Subspace

When the mainlobe interference with high power exists in the space, the traditional interference suppression methods based on eigen-projection have some problems, such as mainlobe peak shift, shallow sidelobe notches and so on. In view of the above problems, a new method for suppressing mainlobe interference based on eigen-subspace is proposed in this paper. Firstly, the eigenvectors of mainlobe and sidelobe interferences are determined according to Akaike Information Criterion (AIC) and correlation discrimination. Secondly, the mainlobe interference component is suppressed by eigen-projection preprocessing. Finally, by defining a new configuration method of weight vectors, adaptive beamforming is performed under the constraint of mainlobe subspace and the linear constraints of sidelobe interferences. Simulation results show that the proposed method achieves a more robust pattern preserving effect, obtains a notch gain close to the ideal zero value of each interference, and improves the output Signal to Interference plus Noise Ratio (SINR) by more than 1.3dB.