Open AccessImproved OP approach utilising correlated projection and eigenspace processing for robust adaptive beamforming
Author(s) -
Yan Lu,
Piao Shengchun,
Xu Feng,
Yang Juan
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.1337
Subject(s) - adaptive beamformer , covariance matrix , robustness (evolution) , estimator , subspace topology , orthographic projection , eigenvalues and eigenvectors , projection (relational algebra) , array processing , algorithm , beamforming , signal processing , computer science , sample mean and sample covariance , signal subspace , noise (video) , mathematics , artificial intelligence , digital signal processing , statistics , telecommunications , image (mathematics) , biochemistry , chemistry , physics , quantum mechanics , computer hardware , gene
The capability of orthogonal projection (OP) approach degrades severely in the presence of array model mismatch, especially when the training samples are mixed with the strong desired signal. Therefore, an improved OP robust adaptive beamforming is proposed based on correlated projection and eigenspace processing in wide input signal‐to‐noise ratio to remove the desired signal self‐null effect and improve robustness. In the proposed approach, the interference subspace is constructed combining the correlated projection and eigenspace processing first. Then, the interference‐plus‐noise covariance matrix is accurately reconstructed via super‐resolution spatial spectrum estimator to eliminate desired signal from sample covariance matrix. Subsequently, the desired signal steering vector is corrected applying correlated projection and then the adaptive weighted vector is modified by OP approach. Simulation results demonstrate that the capability of the proposed approach is almost consistently same as the optimal beamformer in many scenarios.