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.