A low-complexity Laguerre wideband beamformer

Laguerre spatial-temporal processing is a well-known method used to design a wideband beamformer. In this paper, a generalized wideband reduced-rank Laguerre beamformer (RRLB) is proposed. The RRLB uses a reduced-rank transform matrix to estimate the signal subspace and reduces the scale of the received data, which reduces the complexity of obtaining the adaptive weights. The reduced-rank matrix is usually constructed by the eigenvector of the covariance matrix, while the eigenvectors are obtained via eigen decomposition with a high computational load. To reduce the complexity of eigen-decomposition, a fast reduced-rank Laguerre beamforming (FRRLB) algorithm is proposed. In the estimated covariance matrix case, a set of received data vectors is used to construct the reduced-rank matrix for an approximate but fast estimate of the interference subspace. With undistorted response to the desired signal and satisfactory anti-jamming capability, the FRRLB reduces the computational complexity of the adaptive weight approach. The simulation results highlight the validity and effectiveness of the proposed methods. (C) 2018 Elsevier Inc. All rights reserved.