Covariance Matrix Reconstruction With Interference Steering Vector and Power Estimation for Robust Adaptive Beamforming

• Published in: IEEE transactions on vehicular technology Vol. 67; no. 9; pp. 8495 - 8503
• Main Authors: Zheng, Zhi, Zheng, Yan, Wang, Wen-Qin, Zhang, Hongbo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Array signal processing; Beamforming; Computational geometry; Computer simulation; Convexity; Covariance matrices; Covariance matrix; covariance matrix reconstruction; Estimation; Interference; interference-plus-noise covariance matrix; Mathematical analysis; Matrix algebra; Matrix methods; Mobile communication systems; Optimization; Orthogonality; power estimation; Reconstruction; Robust adaptive beamforming; Robustness; Signal quality; Signal to noise ratio; Steering; steering vector estimation
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2849646

To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector, in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches.