Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm

A widely linear extension of the linearly and quadratically constrained minimum variance (LQCMV) beam-former is presented. By exploiting the pseudocovariance matrix which is complementary second order statistics for the ordinary covariance matrix, the widely linear LQCMV (WL-LQCMV) beamformer attains better performance when the received data is noncircular. Adaptive implementation of WL-LQCMV by the dual-domain adaptive algorithm (DDAA) brings a remarkable advantage of fast convergence. The key points of the convergence analysis of DDAA are elaborated. The simulation results are presented to show the efficacy of our approach.