Paper Info
Title
Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming
Abstract
Recently, a number of robust adaptive beamforming (RAB) methods based on Capon power spectrum estimator integrated over a specific region for covariance matrix reconstruction have been proposed. However, all of these methods ignore the residual noise existing in the Capon spectrum estimator, which results in reconstruction errors. In this paper, we propose a RAB algorithm via residual noise elimination and interference powers estimation to reconstruct covariance matrix. First, the proposed algorithm demonstrates the existence of residual noise and analyze its relationship to actual noise. Then, after eliminating the residual noise, the modified Capon power spectrum estimator is utilized to reconstruct the covariance matrix and desired signal SV. Moreover, to reduce the influence of the desired signal on interference powers estimation, we project the snapshots onto the complementary subspace of the desired signal and estimated interference powers are derived according to the theoretical formulation of the interference covariance matrix (ICM). The simulation results demonstrate that the proposed method is robust against various mismatches and can achieve superior performance.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2912402
IEEE ACCESS
Keywords
Field
DocType
Robust adaptive beamforming (RAB),steering vector (SV) estimation,powers estimation,covariance matrix reconstruction
Adaptive beamformer,Subspace topology,Computer science,Signal-to-noise ratio,Algorithm,Spectral density,Capon,Interference (wave propagation),Covariance matrix,Distributed computing,Estimator
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xingyu Zhu111.04
Zhongfu Ye237949.33
Xu Xu316114.26
Rui Zheng410616.04