Paper Info
Title
Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm
Abstract
A widely linear extension of the linearly and quadratically constrained minimum variance (LQCMV) beam-former is presented. By exploiting the pseudocovariance matrix which is complementary second order statistics for the ordinary covariance matrix, the widely linear LQCMV (WL-LQCMV) beamformer attains better performance when the received data is noncircular. Adaptive implementation of WL-LQCMV by the dual-domain adaptive algorithm (DDAA) brings a remarkable advantage of fast convergence. The key points of the convergence analysis of DDAA are elaborated. The simulation results are presented to show the efficacy of our approach.
Year
DOI
Venue
2013
10.1109/ICICS.2013.6782920
ICICS
Keywords
Field
DocType
array signal processing,covariance matrices,statistical analysis,ddaa,wl-lqcmv beamformer,adaptive implementation,augmented dual domain adaptive algorithm,dual domain adaptive algorithm,linearly and quadratically constrained minimum variance,ordinary covariance matrix,pseudocovariance matrix,widely linear lqcmv beamformer
Convergence (routing),Minimum-variance unbiased estimator,Quadratic growth,Mathematical optimization,Adaptive beamformer,Computer science,Matrix (mathematics),Algorithm,Linear extension,Adaptive algorithm,Covariance matrix,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4799-0433-4
1
0.36
References 
Authors
6
2
Name
Order
Citations
PageRank
Masahiro Yukawa127230.44
Saito, Y.210.36