Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm

Masahiro Yukawa; Yuki Saito

doi:10.1109/ICICS.2013.6782920

Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm

Masahiro Yukawa, Yuki Saito

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
DOIs	https://doi.org/10.1109/ICICS.2013.6782920
Publication status	Published - 2013 Jan 1
Event	9th International Conference on Information, Communications and Signal Processing, ICICS 2013 - Tainan, Taiwan, Province of China Duration: 2013 Dec 10 → 2013 Dec 13

Other

Other	9th International Conference on Information, Communications and Signal Processing, ICICS 2013
Country/Territory	Taiwan, Province of China
City	Tainan
Period	13/12/10 → 13/12/13

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

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10.1109/ICICS.2013.6782920

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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) 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.",

author = "Masahiro Yukawa and Yuki Saito",

year = "2013",

month = jan,

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doi = "10.1109/ICICS.2013.6782920",

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T1 - Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm

AU - Yukawa, Masahiro

AU - Saito, Yuki

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N2 - 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.

AB - 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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T2 - 9th International Conference on Information, Communications and Signal Processing, ICICS 2013

Y2 - 10 December 2013 through 13 December 2013

ER -

Widely linear LQCMV beamformer and augmented dual-domain adaptive algorithm

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