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Abstract
The worstcase robust adaptive beamforming problem for generalrank signal model is considered. This is a nonconvex problem, and an approximate version of it (obtained by introducing a matrix decomposition on the presumed covariance matrix of the desired signal) has been well studied in the literature. Different from the existing literature, herein however the original beamforming problem is tackled. Resorting to the strong duality of linear conic programming, the robust adaptive beamforming problem for generalrank signal model is reformulated into an equivalent quadratic matrix inequality (QMI) problem. By employing a linear matrix inequality (LMI) relaxation technique, the QMI problem is turned into a convex semidefinite programming problem. Using the fact that there is often a positive gap between the QMI problem and its LMI relaxation, an approximation algorithm is proposed to solve the robust adaptive beamforming in the QMI form. Besides, several sufficient optimality conditions for the nonconvex QMI problem are developed To validate our results, simulation examples are presented, which demonstrate the improved performance of the new robust beamformer in terms of the output signaltointerferenceplusnoise ratio.
Original language  English 

Article number  9040677 
Pages (fromto)  22442255 
Number of pages  12 
Journal  IEEE Transactions on Signal Processing 
Volume  68 
DOIs  
Publication status  Published  1 Jan 2020 
MoE publication type  A1 Journal articlerefereed 
Keywords
 approximate algorithm
 generalrank signal model
 global optimality condition
 linear matrix inequality (LMI) relaxation
 quadratic matrix inequality (QMI) problem
 Robust adaptive beamforming
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Projects
 1 Finished

Transmit beamspace for active compressive sensing and communication with multiple waveforms
Kocharlakota, K., Upadhya, K., Li, Y., Rizwan Ullah, R., Gao, R., Vorobyov, S., Ghorbani Veshki, F. & Dosti, E.
01/09/2016 → 25/09/2020
Project: Academy of Finland: Other research funding