Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches

Jing-Ran LIN; Qi-Cong PENG; Qi-Shan HUANG

doi:10.1093/ietfec/e89-a.9.2356

Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches

Jing-Ran LIN, Qi-Cong PENG, Qi-Shan HUANG

Full Text Views

0

Share
Cite this

Summary :

A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.9 pp.2356-2362

Publication Date: 2006/09/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e89-a.9.2356

Type of Manuscript: PAPER

Category: Digital Signal Processing

Cite this

Copy

Jing-Ran LIN, Qi-Cong PENG, Qi-Shan HUANG, "Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches" in IEICE TRANSACTIONS on Fundamentals, vol. E89-A, no. 9, pp. 2356-2362, September 2006, doi: 10.1093/ietfec/e89-a.9.2356.
Abstract: A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.9.2356/_p

Copy

@ARTICLE{e89-a_9_2356,
author={Jing-Ran LIN, Qi-Cong PENG, Qi-Shan HUANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches},
year={2006},
volume={E89-A},
number={9},
pages={2356-2362},
abstract={A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.},
keywords={},
doi={10.1093/ietfec/e89-a.9.2356},
ISSN={1745-1337},
month={September},}

Copy

TY - JOUR
TI - Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2356
EP - 2362
AU - Jing-Ran LIN
AU - Qi-Cong PENG
AU - Qi-Shan HUANG
PY - 2006
DO - 10.1093/ietfec/e89-a.9.2356
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2006
AB - A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.
ER -