A Novel Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction over Annulus Uncertainty Sets

Xiao Lei YUAN  Lu GAN  Hong Shu LIAO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E99-A   No.7   pp.1473-1477
Publication Date: 2016/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E99.A.1473
Type of Manuscript: LETTER
Category: Digital Signal Processing
robust adaptive beamforming,  interference covariance matrix reconstruction,  steering vector random error,  annulus uncertainty set,  

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In this letter, a novel robust adaptive beamforming algorithm is addressed to improve the robustness against steering vector random errors (SVREs), which eliminates the signal of interest (SOI) component from the sample covariance matrix (SCM), based on interference-plus-noise covariance matrix (IPNCM) reconstruction over annulus uncertainty sets. Firstly, several annulus uncertainty sets are used to constrain the steering vectors (SVs) of both interferences and the SOI. Additionally the IPNCM is reconstructed according to its definition by estimating each interference SV over its own annulus uncertainty set via the subspace projection algorithm. Meanwhile, the SOI SV is estimated as the prime eigenvector of the SOI covariance matrix term calculated over its own annulus uncertainty set. Finally, a novel robust beamformer is formulated based on the new IPNCM and the SOI SV, and it outperforms other existing reconstruction-based beamformers when the SVREs exist, especially in low input signal-to-noise ratio (SNR) cases, which is proved through the simulation results.