Low-Complexity Covariance Matrix Reconstruction Method for Wideband Adaptive Beamforming

The existing wideband adaptive beamforming algorithms suffer severe performance degradation due to overmuch constraints and high computational complexity. Besides, the desired signal is contained in the received signals, which leads to the phenomenon of self-canceling during the beamforming-based interference suppression. In this paper, a covariance matrix reconstruction-based wideband adaptive beamforming algorithm is proposed to maintain excellent interference suppression performance with low computational complexity. Different from the prior methods, a frequency-angle conversion for wideband beamforming is proposed to convert the wideband signal into several narrowband signals. Thus, an interference-plus-noise covariance matrix (IPNCM) for wideband beamforming can be reconstructed by strategies from narrowband beamforming. Meanwhile, a Gauss-Legendre quadrature (GLQ) is introduced to approximate the integral operation, which provides high accuracy and low computational complexity compared to the polynomial summation. Furthermore, a spatial response variation (SRV) constraint is introduced to reduce the number of constraints and obtain more degrees of freedom to promote interference suppression ability. Simulation results demonstrate the effectiveness of the proposed beamformer with low computational complexity.