Robust LSCMA under quadratic constraint

The conventional constrained least squares constant modulus algorithm (LSCMA) can suffer significant performance degradation in the presence of the slight mismatches between the actual and assumed signal steering vectors. In this paper, to combat the mismatches, a novel robust constrained LSCMA is proposed for implementing double constraints with Taylor-series expansion and Lagrange multipliers method, which is based on explicit modeling of uncertainties in the desired signal array response. The proposed robust constrained LSCMA provides an improved robustness against the signal steering vector mismatches, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. Computer simulations demonstrate a visible performance gain of the proposed algorithm compared as linear constrained LSCMA algorithm.