Robust adaptive beam forming against random calibration error via interference-plus-noise covariance matrix reconstruction

Adaptive beamformers based on interference-plus-noise covariance matrix reconstruction can extract the signal of interest (SOI) effectively in the condition of precise array manifold, but suffer performance degradation in the presence of array calibration error. In this paper, a novel interference-plus-noise covariance matrix reconstruction and steering vector estimation method is proposed, which is robust against random mismatches. By transforming the received signal into the time-frequency (TF) domain, the proposed method reconstructs the interference-plus-noise covariance matrix and estimates the steering vector of the SOI using the spatial time-frequency distribution (STFD) matrices. Neither the imprecise prior information about the array manifold nor the sample covariance matrix is utilized in the whole procedure, guaranteeing high estimation accuracy and excellent output performance. Simulation results demonstrate that the proposed method outperforms existing adaptive beamformers, and achieves high output signal-to-interference-plus-noise ratio (SINR) close to the optimal one even in the case of large calibration error.