Automatic robust adaptive beamforming based on latent root regression

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Automatic robust adaptive beamforming based on latent root regression. / Yang, Jun; Ma, Xiaochuan; Hou, Chaohuan; Liu, Yicong.

2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009. 2009. p. 544-548 5161844 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Yang, J, Ma, X, Hou, C & Liu, Y 2009, Automatic robust adaptive beamforming based on latent root regression. in 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009., 5161844, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, pp. 544-548, 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009, Perugia, Italy, 21/06/2009. https://doi.org/10.1109/SPAWC.2009.5161844

APA

Yang, J., Ma, X., Hou, C., & Liu, Y. (2009). Automatic robust adaptive beamforming based on latent root regression. In 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009 (pp. 544-548). [5161844] IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC https://doi.org/10.1109/SPAWC.2009.5161844

Vancouver

Yang J, Ma X, Hou C, Liu Y. Automatic robust adaptive beamforming based on latent root regression. In 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009. 2009. p. 544-548. 5161844. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). https://doi.org/10.1109/SPAWC.2009.5161844

Author

Yang, Jun ; Ma, Xiaochuan ; Hou, Chaohuan ; Liu, Yicong. / Automatic robust adaptive beamforming based on latent root regression. 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009. 2009. pp. 544-548 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

Bibtex

@inproceedings{8971282ee7af4c00be293aa5cb5b9a63,
title = "Automatic robust adaptive beamforming based on latent root regression",
abstract = "In this paper, we describe a fully automatic method using latent root regression based on the generalized sidelobe can-celer (GSC) parameterization of the minimum variance beam-former. The proposed method gives a theoretically optimal solution in mean-squared error (MSE) sense (minimized MSE solution) by choosing a linear combination of individual latent root regression predictors in the GSC formulation. The performance of the resulting beamformer is illustrated via numerical examples and compared with existing automatic diagonal loading techniques including HKB and the general linear combination (GLC) shrinkage-based method. The simulations show that the proposed method usually gives better performance than HKB, meanwhile, is more robust to errors on steering vectors than GLC when the sample sizes are high.",
keywords = "Adaptive beamforming, Latent root regression, Minimum variance beamforming, Robust beamforming",
author = "Jun Yang and Xiaochuan Ma and Chaohuan Hou and Yicong Liu",
year = "2009",
doi = "10.1109/SPAWC.2009.5161844",
language = "English",
isbn = "9781424436965",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
pages = "544--548",
booktitle = "2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009",
note = "2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009 ; Conference date: 21-06-2009 Through 24-06-2009",

}

RIS

TY - GEN

T1 - Automatic robust adaptive beamforming based on latent root regression

AU - Yang, Jun

AU - Ma, Xiaochuan

AU - Hou, Chaohuan

AU - Liu, Yicong

PY - 2009

Y1 - 2009

N2 - In this paper, we describe a fully automatic method using latent root regression based on the generalized sidelobe can-celer (GSC) parameterization of the minimum variance beam-former. The proposed method gives a theoretically optimal solution in mean-squared error (MSE) sense (minimized MSE solution) by choosing a linear combination of individual latent root regression predictors in the GSC formulation. The performance of the resulting beamformer is illustrated via numerical examples and compared with existing automatic diagonal loading techniques including HKB and the general linear combination (GLC) shrinkage-based method. The simulations show that the proposed method usually gives better performance than HKB, meanwhile, is more robust to errors on steering vectors than GLC when the sample sizes are high.

AB - In this paper, we describe a fully automatic method using latent root regression based on the generalized sidelobe can-celer (GSC) parameterization of the minimum variance beam-former. The proposed method gives a theoretically optimal solution in mean-squared error (MSE) sense (minimized MSE solution) by choosing a linear combination of individual latent root regression predictors in the GSC formulation. The performance of the resulting beamformer is illustrated via numerical examples and compared with existing automatic diagonal loading techniques including HKB and the general linear combination (GLC) shrinkage-based method. The simulations show that the proposed method usually gives better performance than HKB, meanwhile, is more robust to errors on steering vectors than GLC when the sample sizes are high.

KW - Adaptive beamforming

KW - Latent root regression

KW - Minimum variance beamforming

KW - Robust beamforming

UR - http://www.scopus.com/inward/record.url?scp=70449553524&partnerID=8YFLogxK

U2 - 10.1109/SPAWC.2009.5161844

DO - 10.1109/SPAWC.2009.5161844

M3 - Article in proceedings

AN - SCOPUS:70449553524

SN - 9781424436965

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

SP - 544

EP - 548

BT - 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009

T2 - 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009

Y2 - 21 June 2009 through 24 June 2009

ER -

ID: 362748562