Abstract:

In practice, due to the fact that the real signal covariance matrix can not be directly acquired, the sample covariance matrix (SCM) is usually used to replace the real covariance matrix, which will cause a large estimation error under the low snapshots condition. According to this problem, a beamforming algorithm for mainlobe interference suppression based on convex constrained Tyler’s estimator is proposed. Firstly, the convex constrained Tyler’s estimator is used to obtain the estimation of signal covariance matrix under low snapshots condition. Secondly, screening the eigenvector of the mainlobe interference by utilizing the multiple signal classification direction of arrival (DOA) estimation algorithm, and then the eigen-projection matrix preprocessing (EMP) method is used to suppress the mainlobe interference. Finally, the adaptive weight vector is obtained by linear constraint method. Simulation results demonstrate that the proposed method can achieve higher effectiveness and stronger robustness under low snapshots condition.