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International Journal of Antennas and Propagation
Volume 2016 (2016), Article ID 2438183, 9 pages
http://dx.doi.org/10.1155/2016/2438183
Research Article

Robust Adaptive Beamforming Using a Low-Complexity Steering Vector Estimation and Covariance Matrix Reconstruction Algorithm

Zhengzhou Institute of Information Science and Technology, Zhengzhou, Henan 450000, China

Received 16 April 2016; Accepted 12 June 2016

Academic Editor: Michelangelo Villano

Copyright © 2016 Pei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel low-complexity robust adaptive beamforming (RAB) technique is proposed in order to overcome the major drawbacks from which the recent reported RAB algorithms suffer, mainly the high computational cost and the requirement for optimization programs. The proposed algorithm estimates the array steering vector (ASV) using a closed-form formula obtained by a subspace-based method and reconstructs the interference-plus-noise (IPN) covariance matrix by utilizing a sampling progress and employing the covariance matrix taper (CMT) technique. Moreover, the proposed beamformer only requires knowledge of the antenna array geometry and prior information of the probable angular sector in which the actual ASV lies. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior performance over the existing RAB methods.