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-interferenceplus-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 r...
