Mainlobe Interference Suppression Based on Compressive Sensing and Covariance Matrix Reconstruction

Abstract

When mainlobe interference exists in space, the traditional anti-interference methods have problems such as peak offset and the performance of sidelobe interference suppression reduction. To solve the above problems, this paper proposes a mainlobe interference suppression method based on compressive sensing and covariance matrix reconstruction. Firstly, an improved compressive sensing algorithm is proposed to accurately estimate the Direction Of Arrival of sources, and then the signal steering vectors and signal subspaces can be established. The mainlobe interference can be suppressed by establishing an oblique projection operator through signal subspaces. Meanwhile, the sidelobe-interference-noise covariance matrix can be reconstructed by the steering vectors, and then the adaptive weight vector is obtained. Simulation results show that the proposed method can form a more robust beam pattern and has better output performance. The proposed method is still effective when the desired signal exists in the received signal.