Robust Adaptive Beamforming for Uniform Linear Arrays With Sensor Gain and Phase Uncertainties

被引:9
|
作者
Yang, Long [1 ]
Yang, Yixin [1 ]
Yang, Jie [1 ]
机构
[1] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Shaanxi, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
基金
中国国家自然科学基金;
关键词
Covariance matrix reconstruction; robust adaptive beamforming; sensor gain and phase calibration; non-uniform noise environment; COVARIANCE-MATRIX RECONSTRUCTION; HIGH-ORDER SUPERDIRECTIVITY;
D O I
10.1109/ACCESS.2018.2886405
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A robust adaptive beamforming method is proposed in this paper for uniform linear arrays with respect to sensor gain and phase uncertainties. The sensor gain and phase parameters are obtained by solving a series of linear equations that describe the specific structure of the array covariance matrix for a uniform linear array. Partly calibrated parameter constraints are required due to the rank defect of the coefficient matrix. The necessary condition to enable the partly calibrated sensors to estimate all the unknown gain and phase parameters is also deduced. Sensor noise power, and hence, interference-plus-noise covariance matrix (INCM) can then be calculated with the sensor gain and phase information. The robust adaptive beamformer is finally formed using the reconstructed INCM. In comparison with other reconstruction-based beamformers, the proposed method achieves satisfactory performance when sensor gain and phase uncertainties dominate the steering vector mismatch. The effectiveness of the proposed method is also confirmed by experimental results.
引用
收藏
页码:2677 / 2685
页数:9
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