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Adaptive Beamforming with Robustness against Both FiniteSample Effects and Steering Vector Mismatches
JingRan LIN QiCong PENG QiShan HUANG
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E89A
No.9
pp.23562362 Publication Date: 2006/09/01
Online ISSN: 17451337
DOI: 10.1093/ietfec/e89a.9.2356
Print ISSN: 09168508 Type of Manuscript: PAPER Category: Digital Signal Processing Keyword: robust adaptive beamforming (RABF), diagonal loading, finitesample effects, steering vector mismatches, joint worstcase performance optimization,
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Summary:
A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finitesample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worstcase performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finitesample effects into account and applies worstcase performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worstcase RABF (JWRABF). (2) It suggests a simple closedform 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 closedform solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.

