A New Quadratic Matrix Inequality Approach to Robust Adaptive Beamforming for General-rank Signal Model

The worst-case robust adaptive beamforming problem for generalrank signal model is considered. This is a nonconvex problem, and an approximate version of it (by introducing a matrix decomposition on the presumed covariance matrix of the desired signal) has been studied in the literature. Herein the...

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Veröffentlicht in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 4335 - 4339
Hauptverfasser:	Huang, Yongwei, Vorobyov, Sergiy A., Luo, Zhi-Quan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2019
Schlagworte:	Adaptation models Approximation algorithms Array signal processing Covariance matrices deterministic approximate algorithm general-rank signal model Linear matrix inequalities linear matrix inequality relaxation Matrix decomposition quadratic matrix inequality problem Robust adaptive beamforming Semidefinite programming Signal processing algorithms Signal to noise ratio Speech processing
ISSN:	2379-190X
Online-Zugang:	Volltext
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