Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint

Abstract

We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.

Keywords

Beamforming / General-rank / Low complexity / Positive semidefinite (PSD) constraint / Model mismatches

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2016 Zhejiang University and Springer-Verlag Berlin Heidelberg