Widely Linear LQCMV Beamformer and Augmented Dual-Domain Adaptive Algorithm

被引：0
|
作者：
Yukawa, Masahiro [1 ]
Saito, Yuki [2 ]
机构：
[1] Keio Univ, Dept Elect & Elect Engn, Tokyo 108, Japan
[2] Niigata Univ, Dept Elect & Elect Engn, Niigata 95021, Japan
来源：
2013 9TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING (ICICS) | 2013年
关键词：
PROJECTED SUBGRADIENT METHOD; SET;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A widely linear extension of the linearly and quadratically constrained minimum variance (LQCMV) beamformer 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.
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页数：5
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