Open AccessA Two‐Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming
Author(s) -
WANG Jie,
YANG Guangquan,
HU Yi,
ZHANG Chunliang
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.06.016
Subject(s) - capon , shrinkage , stage (stratigraphy) , nonlinear system , covariance matrix , beamforming , mathematics , sample (material) , computer science , algorithm , statistics , geology , physics , quantum mechanics , paleontology , thermodynamics
When the number of snapshots used to estimate the Sample covariance matrix (SCM) approaches infinity and the array steering vector is accurately known, the Standard Capon beamformer (SCB) can better suppress spatial noises than data‐independent beamformers. On the contrary, the performance of the SCB may decrease. To solve this problem, we propose a two‐stage shrinkage scheme for the SCM. Specifically, in the first stage, the SCM is enhanced by the General linear combination (GLC) method, which will be referred to as GLC‐SCM; and in the second stage, the GLCSCM is further improved with the Exponential matrix (EM) method, which will be referred to as GLC‐EM‐SCM. Compared with the conventional methods, the proposed method can achieve higher signal‐to‐interference‐noise ratio output and more accurate signal power estimate.