Mainlobe maintenance using shrinkage estimator method
Author(s) -
Sun Chenwei,
Tao Haihong,
Song Jiaqi,
Zhao Langxu
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0691
Subject(s) - covariance matrix , algorithm , robustness (evolution) , adaptive beamformer , jamming , estimator , identity matrix , computer science , shrinkage estimator , scatter matrix , diagonal matrix , matrix (chemical analysis) , covariance , estimation of covariance matrices , mathematics , diagonal , minimum variance unbiased estimator , beamforming , statistics , bias of an estimator , eigenvalues and eigenvectors , chemistry , biochemistry , quantum mechanics , thermodynamics , physics , gene , materials science , composite material , geometry
A mainlobe maintenance method based on shrinkage estimator is presented here to promote the adaptive digital beamforming performance when there exists mainlobe jamming (MLJ). First, block matrix preprocessing (BMP) method is applied to suppress the MLJ. Then, the linear combination of estimated covariance matrix and identity matrix is optimised to generate more accurate estimation of the covariance matrix. After that, the improved covariance matrix is utilised to generate the adaptive weights to suppress the sidelobe jamming. Finally, the simulation shows that the proposed method is capable of eliminating peak offset of mainlobe and high sidelobes introduced by BMP and provides robustness against finite data samples effects. Accordingly, it outperforms noise whitening with error compensation, diagonal loading, and robust covariance matrix reconstruction in output SINR.
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