MASS-RAB: Robust adaptive beamforming for general-rank signal models via matched spatial spectrum processing

The wavefront of acoustic signal suffers from fast fluctuation after a long distance propagation in a random and inhomogeneous ocean channel, which makes the rank of the covariance matrix for the desired signal (signal of interest) remarkably higher than one. Consequently, the assumption of rank-one point signal model for existing adaptive beamforming algorithms is no longer suitable. In this paper, a matched spatial spectrum processing based robust adaptive beamforming (MASS-RAB) algorithm is presented for general-rank signal models. First, the interference-plus-noise covariance matrix and the desired signal covariance matrix are reconstructed using the matched spatial spectrum processing method. Second, the weight vector is directly calculated using these reconstructed covariance matrices for the minimum variance distortionless response (MVDR) algorithm, which is developed for the general-rank signal models. Due to covariance matrix reconstruction, the MASS-RAB algorithm is more robust than those methods relying on the sample covariance matrix. The cases of the rank-one point signal model and the full-rank non-point signal model are considered by several numerical examples. Experimental results have demonstrated the superiority of the proposed MASS-RAB method.