A Novel Beamforming Technique: Introducing a Convex Constrained Optimization and Compressed-Sensing Model

In this article, the problem of providing robustness to the array receiver configuration so as to achieve robust mainlobe gain and low sidelobes under the highly dynamic condition is addressed. We introduce a novel convex constrained optimization and compressed-sensing (CS) beamforming model (CCOB-CS) in which spatially sparse signals are reconstructed using an orthogonal matching pursuit (OMP) algorithm. The desired signal beam gain can be obtained using the convex constrained optimization beamforming (CCOB) algorithm. MATLAB software is used for the Monte Carlo tests, which are performed for four different array element conditions.