Robust algorithm for linearly constrained constant modulus

Linearly constrained constant modulus algorithm (CMA) is an effective blind adaptive beamforming. However, the constrained CMA suffers from severe performance degradation even if there is a small mismatch in the desired signal. In this paper, we propose a new approach to linearly constrained CMA that provides increased robustness against the mismatch problem. We modify the conventional CMA cost function by including a regularization term that penalizes differences between the actual and assumed array responses. The proposed algorithm can provides the significant improvement of the adaptive array performance and yield excellent signal capture ability. Simulation results are presented that illustrate the advantages of the proposed algorithm.