Robust Least Squares Constant Modulus Beamforming with Sparse Constraint

Abstract

Beamforming with sparse constraint has shown important performance improvement. In this paper, a robust least squares constant modulus beamforming with sparse constraint is proposed. The proposed approach shows a faster convergence performance, lower sidelobe level, and better steady-state output signal-to-interference-plus-noise ratio (SINR) than that of the conventional linearly constrained least squares constant modulus algorithm, especially in the presence of mismatches between the actual and presumed steering vectors. Simulation results demonstrate the effectiveness of the proposed method.