A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

Di YAO; Xin ZHANG; Qiang YANG; Weibo DENG

doi:10.1587/transfun.E100.A.3049

A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

Di YAO, Xin ZHANG, Qiang YANG, Weibo DENG

Full Text Views

0

Share
Cite this

Summary :

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.12 pp.3049-3053

Publication Date: 2017/12/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.3049

Type of Manuscript: LETTER

Category: Digital Signal Processing

Authors

Di YAO
  Harbin Institute of Technology
Xin ZHANG
  Harbin Institute of Technology
Qiang YANG
  Harbin Institute of Technology
Weibo DENG
  Harbin Institute of Technology

Keyword

covariance matrix construction, array steering vector estimation, compressed sensing, total least squares

Cite this

Copy

Di YAO, Xin ZHANG, Qiang YANG, Weibo DENG, "A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 12, pp. 3049-3053, December 2017, doi: 10.1587/transfun.E100.A.3049.
Abstract: An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.3049/_p

Copy

@ARTICLE{e100-a_12_3049,
author={Di YAO, Xin ZHANG, Qiang YANG, Weibo DENG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing},
year={2017},
volume={E100-A},
number={12},
pages={3049-3053},
abstract={An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.},
keywords={},
doi={10.1587/transfun.E100.A.3049},
ISSN={1745-1337},
month={December},}

Copy

TY - JOUR
TI - A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3049
EP - 3053
AU - Di YAO
AU - Xin ZHANG
AU - Qiang YANG
AU - Weibo DENG
PY - 2017
DO - 10.1587/transfun.E100.A.3049
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2017
AB - An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.
ER -