Mainlobe interference suppression via eigen-projection processing and covariance matrix sparse reconstruction

Abstract

In this paper, a novel mainlobe interference suppression method via eigen-projection processing and covariance matrix sparse reconstruction is proposed, which is able to work when the desired signal is present in the training data. Firstly, the proposed method uses the spatial spectrum algorithm to estimate the direction of arrival (DOA) of sources and the power of sources can be estimated by compressive sensing (CS). Then, the eigen-projection matrix is calculated via the result of DOA to suppress the mainlobe interference in echo data. Finally, adaptive weight vector is obtained by SINCM reconstruction. Compared with other methods, the proposed method can achieve better performance and stronger robustness.