  The computational complexity of the conventional adaptive beamformer is
relatively large, and the performance degrades significantly due to the model
mismatch errors and the unwanted signals in received data. In this paper, an
efficient unwanted signal removal and Gauss-Legendre quadrature (URGLQ)-based
covariance matrix reconstruction method is proposed. Different from the prior
covariance matrix reconstruction methods, a projection matrix is constructed to
remove the unwanted signal from the received data, which improves the
reconstruction accuracy of the covariance matrix. Considering that the
computational complexity of most matrix reconstruction algorithms is relatively
large due to the integral operation, we proposed a Gauss-Legendre
quadrature-based method to approximate the integral operation while maintaining
accuracy. Moreover, to improve the robustness of the beamformer, the mismatch
in the desired steering vector is corrected by maximizing the output power of
the beamformer under a constraint that the corrected steering vector cannot
converge to any interference steering vector. Simulation results and prototype
experiments demonstrate that the performance of the proposed beamformer
outperforms the compared methods and is much closer to the optimal beamformer
in different scenarios.
